Imbalanced multiclass datasets pose challenges for machine learning algorithms.They often contain minority classes that are important for accurate predictions.However,when the data is sparsely distributed and overlaps...Imbalanced multiclass datasets pose challenges for machine learning algorithms.They often contain minority classes that are important for accurate predictions.However,when the data is sparsely distributed and overlaps with data points fromother classes,it introduces noise.As a result,existing resamplingmethods may fail to preserve the original data patterns,further disrupting data quality and reducingmodel performance.This paper introduces Neighbor Displacement-based Enhanced Synthetic Oversampling(NDESO),a hybridmethod that integrates a data displacement strategy with a resampling technique to achieve data balance.It begins by computing the average distance of noisy data points to their neighbors and adjusting their positions toward the center before applying random oversampling.Extensive evaluations compare 14 alternatives on nine classifiers across synthetic and 20 real-world datasetswith varying imbalance ratios.This evaluation was structured into two distinct test groups.First,the effects of k-neighbor variations and distance metrics are evaluated,followed by a comparison of resampled data distributions against alternatives,and finally,determining the most suitable oversampling technique for data balancing.Second,the overall performance of the NDESO algorithm was assessed,focusing on G-mean and statistical significance.The results demonstrate that our method is robust to a wide range of variations in these parameters and the overall performance achieves an average G-mean score of 0.90,which is among the highest.Additionally,it attains the lowest mean rank of 2.88,indicating statistically significant improvements over existing approaches.This advantage underscores its potential for effectively handling data imbalance in practical scenarios.展开更多
When dealing with imbalanced datasets,the traditional support vectormachine(SVM)tends to produce a classification hyperplane that is biased towards the majority class,which exhibits poor robustness.This paper proposes...When dealing with imbalanced datasets,the traditional support vectormachine(SVM)tends to produce a classification hyperplane that is biased towards the majority class,which exhibits poor robustness.This paper proposes a high-performance classification algorithm specifically designed for imbalanced datasets.The proposed method first uses a biased second-order cone programming support vectormachine(B-SOCP-SVM)to identify the support vectors(SVs)and non-support vectors(NSVs)in the imbalanced data.Then,it applies the synthetic minority over-sampling technique(SV-SMOTE)to oversample the support vectors of the minority class and uses the random under-sampling technique(NSV-RUS)multiple times to undersample the non-support vectors of the majority class.Combining the above-obtained minority class data set withmultiple majority class datasets can obtainmultiple new balanced data sets.Finally,SOCP-SVM is used to classify each data set,and the final result is obtained through the integrated algorithm.Experimental results demonstrate that the proposed method performs excellently on imbalanced datasets.展开更多
Despite the widespread use of Decision trees (DT) across various applications, their performance tends to suffer when dealing with imbalanced datasets, where the distribution of certain classes significantly outweighs...Despite the widespread use of Decision trees (DT) across various applications, their performance tends to suffer when dealing with imbalanced datasets, where the distribution of certain classes significantly outweighs others. Cost-sensitive learning is a strategy to solve this problem, and several cost-sensitive DT algorithms have been proposed to date. However, existing algorithms, which are heuristic, tried to greedily select either a better splitting point or feature node, leading to local optima for tree nodes and ignoring the cost of the whole tree. In addition, determination of the costs is difficult and often requires domain expertise. This study proposes a DT for imbalanced data, called Swarm-based Cost-sensitive DT (SCDT), using the cost-sensitive learning strategy and an enhanced swarm-based algorithm. The DT is encoded using a hybrid individual representation. A hybrid artificial bee colony approach is designed to optimize rules, considering specified costs in an F-Measure-based fitness function. Experimental results using datasets compared with state-of-the-art DT algorithms show that the SCDT method achieved the highest performance on most datasets. Moreover, SCDT also excels in other critical performance metrics, such as recall, precision, F1-score, and AUC, with notable results with average values of 83%, 87.3%, 85%, and 80.7%, respectively.展开更多
Financial distress prediction(FDP)is a critical area of study for researchers,industry stakeholders,and regulatory authorities.However,FDP tasks present several challenges,including high-dimensional datasets,class imb...Financial distress prediction(FDP)is a critical area of study for researchers,industry stakeholders,and regulatory authorities.However,FDP tasks present several challenges,including high-dimensional datasets,class imbalances,and the complexity of parameter optimization.These issues often hinder the predictive model’s ability to accurately identify companies at high risk of financial distress.To mitigate these challenges,we introduce FinMHSPE—a novel multi-heterogeneous self-paced ensemble(MHSPE)FDP learning framework.The proposed model uses pairwise comparisons of data from multiple time frames combined with the maximum relevance and minimum redundancy method to select an optimal subset of features,effectively resolving the high dimensionality issue.Furthermore,the proposed framework incorporates the MHSPE model to iteratively identify the most informative majority class data samples,effectively addressing the class imbalance issue.To optimize the model’s parameters,we leverage the particle swarm optimization algorithm.The robustness of our proposed model is validated through extensive experiments performed on a financial dataset of Chinese listed companies.The empirical results demonstrate that the proposed model outperforms existing competing models in the field of FDP.Specifically,our FinMHSPE framework achieves the highest performance,achieving an area under the curve(AUC)value of 0.9574,considerably surpassing all existing methods.A comparative analysis of AUC values further reveals that FinMHSPE outperforms state-of-the-art approaches that rely on financial features as inputs.Furthermore,our investigation identifies several valuable features for enhancing FDP model performance,notably those associated with a company’s information and growth potential.展开更多
Imbalanced loads in freight railway vehicles pose significant risks to vehicle running safety as well as track integrity,increasing the likelihood of derailments and increasing track wear rate.This study presents a ro...Imbalanced loads in freight railway vehicles pose significant risks to vehicle running safety as well as track integrity,increasing the likelihood of derailments and increasing track wear rate.This study presents a robust machine learning-based methodology designed to detect and classify transverse imbalances in freight vehicles using dynamic rail responses.The proposed approach employs wayside monitoring systems with accelerometers and strain gauges,integrating advanced feature extraction methods,including principal component analysis,log-mel spectrograms,and multi-feature-based techniques.The methodology enhances detection accuracy by normalizing features to eliminate environmental and operational variations and employing data fusion for sensitive index creation.It is capable of distinguishing between different severity levels of imbalanced loads across various wagon types.By simulating scenarios with typical European freight wagons,the study demonstrates the effectiveness of the approach,offering a valuable tool for railway infrastructure managers to mitigate risks associated with imbalanced loads.This research contributes to the field by providing a scalable,non-invasive solution for real-time monitoring and safety enhancement in freight rail operations.展开更多
For imbalanced datasets, the focus of classification is to identify samples of the minority class. The performance of current data mining algorithms is not good enough for processing imbalanced datasets. The synthetic...For imbalanced datasets, the focus of classification is to identify samples of the minority class. The performance of current data mining algorithms is not good enough for processing imbalanced datasets. The synthetic minority over-sampling technique(SMOTE) is specifically designed for learning from imbalanced datasets, generating synthetic minority class examples by interpolating between minority class examples nearby. However, the SMOTE encounters the overgeneralization problem. The densitybased spatial clustering of applications with noise(DBSCAN) is not rigorous when dealing with the samples near the borderline.We optimize the DBSCAN algorithm for this problem to make clustering more reasonable. This paper integrates the optimized DBSCAN and SMOTE, and proposes a density-based synthetic minority over-sampling technique(DSMOTE). First, the optimized DBSCAN is used to divide the samples of the minority class into three groups, including core samples, borderline samples and noise samples, and then the noise samples of minority class is removed to synthesize more effective samples. In order to make full use of the information of core samples and borderline samples,different strategies are used to over-sample core samples and borderline samples. Experiments show that DSMOTE can achieve better results compared with SMOTE and Borderline-SMOTE in terms of precision, recall and F-value.展开更多
Imbalanced data is one type of datasets that are frequently found in real-world applications,e.g.,fraud detection and cancer diagnosis.For this type of datasets,improving the accuracy to identify their minority class ...Imbalanced data is one type of datasets that are frequently found in real-world applications,e.g.,fraud detection and cancer diagnosis.For this type of datasets,improving the accuracy to identify their minority class is a critically important issue.Feature selection is one method to address this issue.An effective feature selection method can choose a subset of features that favor in the accurate determination of the minority class.A decision tree is a classifier that can be built up by using different splitting criteria.Its advantage is the ease of detecting which feature is used as a splitting node.Thus,it is possible to use a decision tree splitting criterion as a feature selection method.In this paper,an embedded feature selection method using our proposed weighted Gini index(WGI)is proposed.Its comparison results with Chi2,F-statistic and Gini index feature selection methods show that F-statistic and Chi2 reach the best performance when only a few features are selected.As the number of selected features increases,our proposed method has the highest probability of achieving the best performance.The area under a receiver operating characteristic curve(ROC AUC)and F-measure are used as evaluation criteria.Experimental results with two datasets show that ROC AUC performance can be high,even if only a few features are selected and used,and only changes slightly as more and more features are selected.However,the performance of Fmeasure achieves excellent performance only if 20%or more of features are chosen.The results are helpful for practitioners to select a proper feature selection method when facing a practical problem.展开更多
Imbalanced data classification is an important research topic in real-world applications,like fault diagnosis in an aircraft manufacturing system.The over-sampling method is often used to solve this problem.It generat...Imbalanced data classification is an important research topic in real-world applications,like fault diagnosis in an aircraft manufacturing system.The over-sampling method is often used to solve this problem.It generates samples according to the distance between minority data.However,the traditional over-sampling method may change the original data distribution,which is harmful to the classification performance.In this paper,we propose a new method called Conditional SelfAttention Generative Adversarial Network with Differential Evolution(CSAGAN-DE)for imbalanced data classification.The new method aims at improving the classification performance of minority data by enhancing the quality of the generation of minority data.In CSAGAN-DE,the minority data are fed into the self-attention generative adversarial network to approximate the data distribution and create new data for the minority class.Then,the differential evolution algorithm is employed to automatically determine the number of generated minority data for achieving a satisfactory classification performance.Several experiments are conducted to evaluate the performance of the new CSAGAN-DE method.The results show that the new method can efficiently improve the classification performance compared with other related methods.展开更多
The extreme imbalanced data problem is the core issue in anomaly detection.The amount of abnormal data is so small that we cannot get adequate information to analyze it.The mainstream methods focus on taking fully adv...The extreme imbalanced data problem is the core issue in anomaly detection.The amount of abnormal data is so small that we cannot get adequate information to analyze it.The mainstream methods focus on taking fully advantages of the normal data,of which the discrimination method is that the data not belonging to normal data distribution is the anomaly.From the view of data science,we concentrate on the abnormal data and generate artificial abnormal samples by machine learning method.In this kind of technologies,Synthetic Minority Over-sampling Technique and its improved algorithms are representative milestones,which generate synthetic examples randomly in selected line segments.In our work,we break the limitation of line segment and propose an Imbalanced Triangle Synthetic Data method.In theory,our method covers a wider range.In experiment with real world data,our method performs better than the SMOTE and its meliorations.展开更多
Imbalanced data classification is one of the major problems in machine learning.This imbalanced dataset typically has significant differences in the number of data samples between its classes.In most cases,the perform...Imbalanced data classification is one of the major problems in machine learning.This imbalanced dataset typically has significant differences in the number of data samples between its classes.In most cases,the performance of the machine learning algorithm such as Support Vector Machine(SVM)is affected when dealing with an imbalanced dataset.The classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class samples.In this paper,a hybrid approach combining data pre-processing technique andSVMalgorithm based on improved Simulated Annealing(SA)was proposed.Firstly,the data preprocessing technique which primarily aims at solving the resampling strategy of handling imbalanced datasets was proposed.In this technique,the data were first synthetically generated to equalize the number of samples between classes and followed by a reduction step to remove redundancy and duplicated data.Next is the training of a balanced dataset using SVM.Since this algorithm requires an iterative process to search for the best penalty parameter during training,an improved SA algorithm was proposed for this task.In this proposed improvement,a new acceptance criterion for the solution to be accepted in the SA algorithm was introduced to enhance the accuracy of the optimization process.Experimental works based on ten publicly available imbalanced datasets have demonstrated higher accuracy in the classification tasks using the proposed approach in comparison with the conventional implementation of SVM.Registering at an average of 89.65%of accuracy for the binary class classification has demonstrated the good performance of the proposed works.展开更多
When building a classification model,the scenario where the samples of one class are significantly more than those of the other class is called data imbalance.Data imbalance causes the trained classification model to ...When building a classification model,the scenario where the samples of one class are significantly more than those of the other class is called data imbalance.Data imbalance causes the trained classification model to be in favor of the majority class(usually defined as the negative class),which may do harm to the accuracy of the minority class(usually defined as the positive class),and then lead to poor overall performance of the model.A method called MSHR-FCSSVM for solving imbalanced data classification is proposed in this article,which is based on a new hybrid resampling approach(MSHR)and a new fine cost-sensitive support vector machine(CS-SVM)classifier(FCSSVM).The MSHR measures the separability of each negative sample through its Silhouette value calculated by Mahalanobis distance between samples,based on which,the so-called pseudo-negative samples are screened out to generate new positive samples(over-sampling step)through linear interpolation and are deleted finally(under-sampling step).This approach replaces pseudo-negative samples with generated new positive samples one by one to clear up the inter-class overlap on the borderline,without changing the overall scale of the dataset.The FCSSVM is an improved version of the traditional CS-SVM.It considers influences of both the imbalance of sample number and the class distribution on classification simultaneously,and through finely tuning the class cost weights by using the efficient optimization algorithm based on the physical phenomenon of rime-ice(RIME)algorithm with cross-validation accuracy as the fitness function to accurately adjust the classification borderline.To verify the effectiveness of the proposed method,a series of experiments are carried out based on 20 imbalanced datasets including both mildly and extremely imbalanced datasets.The experimental results show that the MSHR-FCSSVM method performs better than the methods for comparison in most cases,and both the MSHR and the FCSSVM played significant roles.展开更多
Encrypted traffic classification has become a hot issue in network security research.The class imbalance problem of traffic samples often causes the deterioration of Machine Learning based classifier performance.Altho...Encrypted traffic classification has become a hot issue in network security research.The class imbalance problem of traffic samples often causes the deterioration of Machine Learning based classifier performance.Although the Generative Adversarial Network(GAN)method can generate new samples by learning the feature distribution of the original samples,it is confronted with the problems of unstable training andmode collapse.To this end,a novel data augmenting approach called Graph CWGAN-GP is proposed in this paper.The traffic data is first converted into grayscale images as the input for the proposed model.Then,the minority class data is augmented with our proposed model,which is built by introducing conditional constraints and a new distance metric in typical GAN.Finally,the classical deep learning model is adopted as a classifier to classify datasets augmented by the Condition GAN(CGAN),Wasserstein GAN-Gradient Penalty(WGAN-GP)and Graph CWGAN-GP,respectively.Compared with the state-of-the-art GAN methods,the Graph CWGAN-GP cannot only control the modes of the data to be generated,but also overcome the problem of unstable training and generate more realistic and diverse samples.The experimental results show that the classification precision,recall and F1-Score of theminority class in the balanced dataset augmented in this paper have improved by more than 2.37%,3.39% and 4.57%,respectively.展开更多
As one important type of post-translational modifications(PTMs),protein lysine succinylation regulates many important biological processes.It is also closely involved with some major diseases in the aspects of Cardiom...As one important type of post-translational modifications(PTMs),protein lysine succinylation regulates many important biological processes.It is also closely involved with some major diseases in the aspects of Cardiometabolic,liver metabolic,nervous system and so on.Therefore,it is imperative to predict the succinylation sites in proteins for both basic research and drug development.In this paper,a novel predictor called i Succ Lys-BLS was proposed by not only introducing a new machine learning algorithm—Broad Learning System,but also optimizing the imbalanced data by randomly labeling samples.Rigorous cross-validation and independent test indicate that the success rate of i Succ Lys-BLS for positive samples is overwhelmingly higher than its counterparts.展开更多
A common difficulty in building prediction models with real-world environmental datasets is the skewed distribution of classes.There are significantly more samples for day-to-day classes,while rare events such as poll...A common difficulty in building prediction models with real-world environmental datasets is the skewed distribution of classes.There are significantly more samples for day-to-day classes,while rare events such as polluted classes are uncommon.Consequently,the limited availability of minority outcomes lowers the classifier’s overall reliability.This study assesses the capability of machine learning(ML)algorithms in tackling imbalanced water quality data based on the metrics of precision,recall,and F1 score.It intends to balance the misled accuracy towards the majority of data.Hence,10 ML algorithms of its performance are compared.The classifiers included are AdaBoost,SupportVector Machine,Linear Discriminant Analysis,k-Nearest Neighbors,Naive Bayes,Decision Trees,Random Forest,Extra Trees,Bagging,and the Multilayer Perceptron.This study also uses the Easy Ensemble Classifier,Balanced Bagging,andRUSBoost algorithm to evaluatemulti-class imbalanced learning methods.The comparison results revealed that a highaccuracy machine learning model is not always good in recall and sensitivity.This paper’s stacked ensemble deep learning(SE-DL)generalization model effectively classifies the water quality index(WQI)based on 23 input variables.The proposed algorithm achieved a remarkable average of 95.69%,94.96%,92.92%,and 93.88%for accuracy,precision,recall,and F1 score,respectively.In addition,the proposed model is compared against two state-of-the-art classifiers,the XGBoost(eXtreme Gradient Boosting)and Light Gradient Boosting Machine,where performance metrics of balanced accuracy and g-mean are included.The experimental setup concluded XGBoost with a higher balanced accuracy and G-mean.However,the SE-DL model has a better and more balanced performance in the F1 score.The SE-DL model aligns with the goal of this study to ensure the balance between accuracy and completeness for each water quality class.The proposed algorithm is also capable of higher efficiency at a lower computational time against using the standard SyntheticMinority Oversampling Technique(SMOTE)approach to imbalanced datasets.展开更多
Purpose: This paper aims to improve the classification performance when the data is imbalanced by applying different sampling techniques available in Machine Learning.Design/methodology/approach: The medical appointme...Purpose: This paper aims to improve the classification performance when the data is imbalanced by applying different sampling techniques available in Machine Learning.Design/methodology/approach: The medical appointment no-show dataset is imbalanced, and when classification algorithms are applied directly to the dataset, it is biased towards the majority class, ignoring the minority class. To avoid this issue, multiple sampling techniques such as Random Over Sampling(ROS), Random Under Sampling(RUS), Synthetic Minority Oversampling TEchnique(SMOTE), ADAptive SYNthetic Sampling(ADASYN), Edited Nearest Neighbor(ENN), and Condensed Nearest Neighbor(CNN) are applied in order to make the dataset balanced. The performance is assessed by the Decision Tree classifier with the listed sampling techniques and the best performance is identified.Findings: This study focuses on the comparison of the performance metrics of various sampling methods widely used. It is revealed that, compared to other techniques, the Recall is high when ENN is applied CNN and ADASYN have performed equally well on the Imbalanced data.Research limitations: The testing was carried out with limited dataset and needs to be tested with a larger dataset.Practical implications: This framework will be useful whenever the data is imbalanced in real world scenarios, which ultimately improves the performance.Originality/value: This paper uses the rebalancing framework on medical appointment no-show dataset to predict the no-shows and removes the bias towards minority class.展开更多
In this paper,an Observation Points Classifier Ensemble(OPCE)algorithm is proposed to deal with High-Dimensional Imbalanced Classification(HDIC)problems based on data processed using the Multi-Dimensional Scaling(MDS)...In this paper,an Observation Points Classifier Ensemble(OPCE)algorithm is proposed to deal with High-Dimensional Imbalanced Classification(HDIC)problems based on data processed using the Multi-Dimensional Scaling(MDS)feature extraction technique.First,dimensionality of the original imbalanced data is reduced using MDS so that distances between any two different samples are preserved as well as possible.Second,a novel OPCE algorithm is applied to classify imbalanced samples by placing optimised observation points in a low-dimensional data space.Third,optimization of the observation point mappings is carried out to obtain a reliable assessment of the unknown samples.Exhaustive experiments have been conducted to evaluate the feasibility,rationality,and effectiveness of the proposed OPCE algorithm using seven benchmark HDIC data sets.Experimental results show that(1)the OPCE algorithm can be trained faster on low-dimensional imbalanced data than on high-dimensional data;(2)the OPCE algorithm can correctly identify samples as the number of optimised observation points is increased;and(3)statistical analysis reveals that OPCE yields better HDIC performances on the selected data sets in comparison with eight other HDIC algorithms.This demonstrates that OPCE is a viable algorithm to deal with HDIC problems.展开更多
Diabetics is one of the world’s most common diseases which are caused by continued high levels of blood sugar.The risk of diabetics can be lowered if the diabetic is found at the early stage.In recent days,several ma...Diabetics is one of the world’s most common diseases which are caused by continued high levels of blood sugar.The risk of diabetics can be lowered if the diabetic is found at the early stage.In recent days,several machine learning models were developed to predict the diabetic presence at an early stage.In this paper,we propose an embedded-based machine learning model that combines the split-vote method and instance duplication to leverage an imbalanced dataset called PIMA Indian to increase the prediction of diabetics.The proposed method uses both the concept of over-sampling and under-sampling along with model weighting to increase the performance of classification.Different measures such as Accuracy,Precision,Recall,and F1-Score are used to evaluate the model.The results we obtained using K-Nearest Neighbor(kNN),Naïve Bayes(NB),Support Vector Machines(SVM),Random Forest(RF),Logistic Regression(LR),and Decision Trees(DT)were 89.32%,91.44%,95.78%,89.3%,81.76%,and 80.38%respectively.The SVM model is more efficient than other models which are 21.38%more than exiting machine learning-based works.展开更多
Estimation of construction parameters is crucial for optimizing tunnel construction schedule.Due to the influence of routine activities and occasional risk events,these parameters are usually correlated and imbalanced...Estimation of construction parameters is crucial for optimizing tunnel construction schedule.Due to the influence of routine activities and occasional risk events,these parameters are usually correlated and imbalanced.To solve this issue,an improved bidirectional generative adversarial network(BiGAN)model with a joint discriminator structure and zero-centered gradient penalty(0-GP)is proposed.In this model,in order to improve the capability of original BiGAN in learning imbalanced parameters,the joint discriminator separately discriminates the routine activities and risk event durations to balance their influence weights.Then,the self-attention mechanism is embedded so that the discriminator can pay more attention to the imbalanced parameters.Finally,the 0-GP is adapted for the loss of the discrimi-nator to improve its convergence and stability.A case study of a tunnel in China shows that the improved BiGAN can obtain parameter estimates consistent with the classical Gauss mixture model,without the need of tedious and complex correlation analysis.The proposed joint discriminator can increase the ability of BiGAN in estimating imbalanced construction parameters,and the 0-GP can ensure the stability and convergence of the model.展开更多
Most modern technologies,such as social media,smart cities,and the internet of things(IoT),rely on big data.When big data is used in the real-world applications,two data challenges such as class overlap and class imba...Most modern technologies,such as social media,smart cities,and the internet of things(IoT),rely on big data.When big data is used in the real-world applications,two data challenges such as class overlap and class imbalance arises.When dealing with large datasets,most traditional classifiers are stuck in the local optimum problem.As a result,it’s necessary to look into new methods for dealing with large data collections.Several solutions have been proposed for overcoming this issue.The rapid growth of the available data threatens to limit the usefulness of many traditional methods.Methods such as oversampling and undersampling have shown great promises in addressing the issues of class imbalance.Among all of these techniques,Synthetic Minority Oversampling TechniquE(SMOTE)has produced the best results by generating synthetic samples for the minority class in creating a balanced dataset.The issue is that their practical applicability is restricted to problems involving tens of thousands or lower instances of each.In this paper,we have proposed a parallel mode method using SMOTE and MapReduce strategy,this distributes the operation of the algorithm among a group of computational nodes for addressing the aforementioned problem.Our proposed solution has been divided into three stages.Thefirst stage involves the process of splitting the data into different blocks using a mapping function,followed by a pre-processing step for each mapping block that employs a hybrid SMOTE algo-rithm for solving the class imbalanced problem.On each map block,a decision tree model would be constructed.Finally,the decision tree blocks would be com-bined for creating a classification model.We have used numerous datasets with up to 4 million instances in our experiments for testing the proposed scheme’s cap-abilities.As a result,the Hybrid SMOTE appears to have good scalability within the framework proposed,and it also cuts down the processing time.展开更多
Imbalance is a distinctive feature of many datasets,and how to make the dataset balanced become a hot topic in the machine learning field.The Synthetic Minority Oversampling Technique(SMOTE)is the classical method to ...Imbalance is a distinctive feature of many datasets,and how to make the dataset balanced become a hot topic in the machine learning field.The Synthetic Minority Oversampling Technique(SMOTE)is the classical method to solve this problem.Although much research has been conducted on SMOTE,there is still the problem of synthetic sample singularity.To solve the issues of class imbalance and diversity of generated samples,this paper proposes a hybrid resampling method for binary imbalanced data sets,RE-SMOTE,which is designed based on the improvements of two oversampling methods parameter-free SMOTE(PF-SMOTE)and SMOTE-Weighted Ensemble Nearest Neighbor(SMOTE-WENN).Initially,minority class samples are divided into safe and boundary minority categories.Boundary minority samples are regenerated through linear interpolation with the nearest majority class samples.In contrast,safe minority samples are randomly generated within a circular range centered on the initial safe minority samples with a radius determined by the distance to the nearest majority class samples.Furthermore,we use Weighted Edited Nearest Neighbor(WENN)and relative density methods to clean the generated samples and remove the low-quality samples.Relative density is calculated based on the ratio of majority to minority samples among the reverse k-nearest neighbor samples.To verify the effectiveness and robustness of the proposed model,we conducted a comprehensive experimental study on 40 datasets selected from real applications.The experimental results show the superiority of radius estimation-SMOTE(RE-SMOTE)over other state-of-the-art methods.Code is available at:https://github.com/blue9792/RE-SMOTE(accessed on 30 September 2024).展开更多
文摘Imbalanced multiclass datasets pose challenges for machine learning algorithms.They often contain minority classes that are important for accurate predictions.However,when the data is sparsely distributed and overlaps with data points fromother classes,it introduces noise.As a result,existing resamplingmethods may fail to preserve the original data patterns,further disrupting data quality and reducingmodel performance.This paper introduces Neighbor Displacement-based Enhanced Synthetic Oversampling(NDESO),a hybridmethod that integrates a data displacement strategy with a resampling technique to achieve data balance.It begins by computing the average distance of noisy data points to their neighbors and adjusting their positions toward the center before applying random oversampling.Extensive evaluations compare 14 alternatives on nine classifiers across synthetic and 20 real-world datasetswith varying imbalance ratios.This evaluation was structured into two distinct test groups.First,the effects of k-neighbor variations and distance metrics are evaluated,followed by a comparison of resampled data distributions against alternatives,and finally,determining the most suitable oversampling technique for data balancing.Second,the overall performance of the NDESO algorithm was assessed,focusing on G-mean and statistical significance.The results demonstrate that our method is robust to a wide range of variations in these parameters and the overall performance achieves an average G-mean score of 0.90,which is among the highest.Additionally,it attains the lowest mean rank of 2.88,indicating statistically significant improvements over existing approaches.This advantage underscores its potential for effectively handling data imbalance in practical scenarios.
基金supported by the Natural Science Basic Research Program of Shaanxi(Program No.2024JC-YBMS-026).
文摘When dealing with imbalanced datasets,the traditional support vectormachine(SVM)tends to produce a classification hyperplane that is biased towards the majority class,which exhibits poor robustness.This paper proposes a high-performance classification algorithm specifically designed for imbalanced datasets.The proposed method first uses a biased second-order cone programming support vectormachine(B-SOCP-SVM)to identify the support vectors(SVs)and non-support vectors(NSVs)in the imbalanced data.Then,it applies the synthetic minority over-sampling technique(SV-SMOTE)to oversample the support vectors of the minority class and uses the random under-sampling technique(NSV-RUS)multiple times to undersample the non-support vectors of the majority class.Combining the above-obtained minority class data set withmultiple majority class datasets can obtainmultiple new balanced data sets.Finally,SOCP-SVM is used to classify each data set,and the final result is obtained through the integrated algorithm.Experimental results demonstrate that the proposed method performs excellently on imbalanced datasets.
文摘Despite the widespread use of Decision trees (DT) across various applications, their performance tends to suffer when dealing with imbalanced datasets, where the distribution of certain classes significantly outweighs others. Cost-sensitive learning is a strategy to solve this problem, and several cost-sensitive DT algorithms have been proposed to date. However, existing algorithms, which are heuristic, tried to greedily select either a better splitting point or feature node, leading to local optima for tree nodes and ignoring the cost of the whole tree. In addition, determination of the costs is difficult and often requires domain expertise. This study proposes a DT for imbalanced data, called Swarm-based Cost-sensitive DT (SCDT), using the cost-sensitive learning strategy and an enhanced swarm-based algorithm. The DT is encoded using a hybrid individual representation. A hybrid artificial bee colony approach is designed to optimize rules, considering specified costs in an F-Measure-based fitness function. Experimental results using datasets compared with state-of-the-art DT algorithms show that the SCDT method achieved the highest performance on most datasets. Moreover, SCDT also excels in other critical performance metrics, such as recall, precision, F1-score, and AUC, with notable results with average values of 83%, 87.3%, 85%, and 80.7%, respectively.
基金China Postdoctoral Science Foundation(No.2023M740237,2024M750254)Postdoctoral Fellowship Program of CPSF(No.GZB20230934)+1 种基金National Natural Science Foundation of China(No.71801113,72401029,72431003)China Scholarship Council(No.202006060162).
文摘Financial distress prediction(FDP)is a critical area of study for researchers,industry stakeholders,and regulatory authorities.However,FDP tasks present several challenges,including high-dimensional datasets,class imbalances,and the complexity of parameter optimization.These issues often hinder the predictive model’s ability to accurately identify companies at high risk of financial distress.To mitigate these challenges,we introduce FinMHSPE—a novel multi-heterogeneous self-paced ensemble(MHSPE)FDP learning framework.The proposed model uses pairwise comparisons of data from multiple time frames combined with the maximum relevance and minimum redundancy method to select an optimal subset of features,effectively resolving the high dimensionality issue.Furthermore,the proposed framework incorporates the MHSPE model to iteratively identify the most informative majority class data samples,effectively addressing the class imbalance issue.To optimize the model’s parameters,we leverage the particle swarm optimization algorithm.The robustness of our proposed model is validated through extensive experiments performed on a financial dataset of Chinese listed companies.The empirical results demonstrate that the proposed model outperforms existing competing models in the field of FDP.Specifically,our FinMHSPE framework achieves the highest performance,achieving an area under the curve(AUC)value of 0.9574,considerably surpassing all existing methods.A comparative analysis of AUC values further reveals that FinMHSPE outperforms state-of-the-art approaches that rely on financial features as inputs.Furthermore,our investigation identifies several valuable features for enhancing FDP model performance,notably those associated with a company’s information and growth potential.
基金CNPq (Brazilian Ministry of Science and Technology Agency), CAPES (Higher Education Improvement Agency), FAPESP (São Paulo Research Foundation) for financial support under grant #2022/130451, VALE Catedra Under Railfinancially supported by Base Funding-UIDB/04708/2020 with https://doi.org/https://doi.org/10.54499/UIDB/04708/2020 and Programmatic Funding-UIDP/04708/2020 with https://doi. org/https://doi.org/10.54499/UIDP/04708/2020 of the CONSTRUCT-Instituto de I&D em Estruturas e Construções-funded by national funds through the FCT/MCTES (PIDDAC)
文摘Imbalanced loads in freight railway vehicles pose significant risks to vehicle running safety as well as track integrity,increasing the likelihood of derailments and increasing track wear rate.This study presents a robust machine learning-based methodology designed to detect and classify transverse imbalances in freight vehicles using dynamic rail responses.The proposed approach employs wayside monitoring systems with accelerometers and strain gauges,integrating advanced feature extraction methods,including principal component analysis,log-mel spectrograms,and multi-feature-based techniques.The methodology enhances detection accuracy by normalizing features to eliminate environmental and operational variations and employing data fusion for sensitive index creation.It is capable of distinguishing between different severity levels of imbalanced loads across various wagon types.By simulating scenarios with typical European freight wagons,the study demonstrates the effectiveness of the approach,offering a valuable tool for railway infrastructure managers to mitigate risks associated with imbalanced loads.This research contributes to the field by providing a scalable,non-invasive solution for real-time monitoring and safety enhancement in freight rail operations.
基金supported by the National Key Research and Development Program of China(2018YFB1003700)the Scientific and Technological Support Project(Society)of Jiangsu Province(BE2016776)+2 种基金the“333” project of Jiangsu Province(BRA2017228 BRA2017401)the Talent Project in Six Fields of Jiangsu Province(2015-JNHB-012)
文摘For imbalanced datasets, the focus of classification is to identify samples of the minority class. The performance of current data mining algorithms is not good enough for processing imbalanced datasets. The synthetic minority over-sampling technique(SMOTE) is specifically designed for learning from imbalanced datasets, generating synthetic minority class examples by interpolating between minority class examples nearby. However, the SMOTE encounters the overgeneralization problem. The densitybased spatial clustering of applications with noise(DBSCAN) is not rigorous when dealing with the samples near the borderline.We optimize the DBSCAN algorithm for this problem to make clustering more reasonable. This paper integrates the optimized DBSCAN and SMOTE, and proposes a density-based synthetic minority over-sampling technique(DSMOTE). First, the optimized DBSCAN is used to divide the samples of the minority class into three groups, including core samples, borderline samples and noise samples, and then the noise samples of minority class is removed to synthesize more effective samples. In order to make full use of the information of core samples and borderline samples,different strategies are used to over-sample core samples and borderline samples. Experiments show that DSMOTE can achieve better results compared with SMOTE and Borderline-SMOTE in terms of precision, recall and F-value.
基金supported in part by the National Science Foundation of USA(CMMI-1162482)
文摘Imbalanced data is one type of datasets that are frequently found in real-world applications,e.g.,fraud detection and cancer diagnosis.For this type of datasets,improving the accuracy to identify their minority class is a critically important issue.Feature selection is one method to address this issue.An effective feature selection method can choose a subset of features that favor in the accurate determination of the minority class.A decision tree is a classifier that can be built up by using different splitting criteria.Its advantage is the ease of detecting which feature is used as a splitting node.Thus,it is possible to use a decision tree splitting criterion as a feature selection method.In this paper,an embedded feature selection method using our proposed weighted Gini index(WGI)is proposed.Its comparison results with Chi2,F-statistic and Gini index feature selection methods show that F-statistic and Chi2 reach the best performance when only a few features are selected.As the number of selected features increases,our proposed method has the highest probability of achieving the best performance.The area under a receiver operating characteristic curve(ROC AUC)and F-measure are used as evaluation criteria.Experimental results with two datasets show that ROC AUC performance can be high,even if only a few features are selected and used,and only changes slightly as more and more features are selected.However,the performance of Fmeasure achieves excellent performance only if 20%or more of features are chosen.The results are helpful for practitioners to select a proper feature selection method when facing a practical problem.
基金partially supported by the Aeronautical Science Foundation of China(No.201920007001)National Natural Science Foundation of China(Nos.U20B2067,61790552 and 61790554)。
文摘Imbalanced data classification is an important research topic in real-world applications,like fault diagnosis in an aircraft manufacturing system.The over-sampling method is often used to solve this problem.It generates samples according to the distance between minority data.However,the traditional over-sampling method may change the original data distribution,which is harmful to the classification performance.In this paper,we propose a new method called Conditional SelfAttention Generative Adversarial Network with Differential Evolution(CSAGAN-DE)for imbalanced data classification.The new method aims at improving the classification performance of minority data by enhancing the quality of the generation of minority data.In CSAGAN-DE,the minority data are fed into the self-attention generative adversarial network to approximate the data distribution and create new data for the minority class.Then,the differential evolution algorithm is employed to automatically determine the number of generated minority data for achieving a satisfactory classification performance.Several experiments are conducted to evaluate the performance of the new CSAGAN-DE method.The results show that the new method can efficiently improve the classification performance compared with other related methods.
基金This research was financially supported by the National Natural Science Foundation of China(Grant No.61379145)the Joint Funds of CETC(Grant No.20166141B020101).
文摘The extreme imbalanced data problem is the core issue in anomaly detection.The amount of abnormal data is so small that we cannot get adequate information to analyze it.The mainstream methods focus on taking fully advantages of the normal data,of which the discrimination method is that the data not belonging to normal data distribution is the anomaly.From the view of data science,we concentrate on the abnormal data and generate artificial abnormal samples by machine learning method.In this kind of technologies,Synthetic Minority Over-sampling Technique and its improved algorithms are representative milestones,which generate synthetic examples randomly in selected line segments.In our work,we break the limitation of line segment and propose an Imbalanced Triangle Synthetic Data method.In theory,our method covers a wider range.In experiment with real world data,our method performs better than the SMOTE and its meliorations.
文摘Imbalanced data classification is one of the major problems in machine learning.This imbalanced dataset typically has significant differences in the number of data samples between its classes.In most cases,the performance of the machine learning algorithm such as Support Vector Machine(SVM)is affected when dealing with an imbalanced dataset.The classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class samples.In this paper,a hybrid approach combining data pre-processing technique andSVMalgorithm based on improved Simulated Annealing(SA)was proposed.Firstly,the data preprocessing technique which primarily aims at solving the resampling strategy of handling imbalanced datasets was proposed.In this technique,the data were first synthetically generated to equalize the number of samples between classes and followed by a reduction step to remove redundancy and duplicated data.Next is the training of a balanced dataset using SVM.Since this algorithm requires an iterative process to search for the best penalty parameter during training,an improved SA algorithm was proposed for this task.In this proposed improvement,a new acceptance criterion for the solution to be accepted in the SA algorithm was introduced to enhance the accuracy of the optimization process.Experimental works based on ten publicly available imbalanced datasets have demonstrated higher accuracy in the classification tasks using the proposed approach in comparison with the conventional implementation of SVM.Registering at an average of 89.65%of accuracy for the binary class classification has demonstrated the good performance of the proposed works.
基金supported by the Yunnan Major Scientific and Technological Projects(Grant No.202302AD080001)the National Natural Science Foundation,China(No.52065033).
文摘When building a classification model,the scenario where the samples of one class are significantly more than those of the other class is called data imbalance.Data imbalance causes the trained classification model to be in favor of the majority class(usually defined as the negative class),which may do harm to the accuracy of the minority class(usually defined as the positive class),and then lead to poor overall performance of the model.A method called MSHR-FCSSVM for solving imbalanced data classification is proposed in this article,which is based on a new hybrid resampling approach(MSHR)and a new fine cost-sensitive support vector machine(CS-SVM)classifier(FCSSVM).The MSHR measures the separability of each negative sample through its Silhouette value calculated by Mahalanobis distance between samples,based on which,the so-called pseudo-negative samples are screened out to generate new positive samples(over-sampling step)through linear interpolation and are deleted finally(under-sampling step).This approach replaces pseudo-negative samples with generated new positive samples one by one to clear up the inter-class overlap on the borderline,without changing the overall scale of the dataset.The FCSSVM is an improved version of the traditional CS-SVM.It considers influences of both the imbalance of sample number and the class distribution on classification simultaneously,and through finely tuning the class cost weights by using the efficient optimization algorithm based on the physical phenomenon of rime-ice(RIME)algorithm with cross-validation accuracy as the fitness function to accurately adjust the classification borderline.To verify the effectiveness of the proposed method,a series of experiments are carried out based on 20 imbalanced datasets including both mildly and extremely imbalanced datasets.The experimental results show that the MSHR-FCSSVM method performs better than the methods for comparison in most cases,and both the MSHR and the FCSSVM played significant roles.
基金supported by the National Natural Science Foundation of China (Grants Nos.61931004,62072250)the Talent Launch Fund of Nanjing University of Information Science and Technology (2020r061).
文摘Encrypted traffic classification has become a hot issue in network security research.The class imbalance problem of traffic samples often causes the deterioration of Machine Learning based classifier performance.Although the Generative Adversarial Network(GAN)method can generate new samples by learning the feature distribution of the original samples,it is confronted with the problems of unstable training andmode collapse.To this end,a novel data augmenting approach called Graph CWGAN-GP is proposed in this paper.The traffic data is first converted into grayscale images as the input for the proposed model.Then,the minority class data is augmented with our proposed model,which is built by introducing conditional constraints and a new distance metric in typical GAN.Finally,the classical deep learning model is adopted as a classifier to classify datasets augmented by the Condition GAN(CGAN),Wasserstein GAN-Gradient Penalty(WGAN-GP)and Graph CWGAN-GP,respectively.Compared with the state-of-the-art GAN methods,the Graph CWGAN-GP cannot only control the modes of the data to be generated,but also overcome the problem of unstable training and generate more realistic and diverse samples.The experimental results show that the classification precision,recall and F1-Score of theminority class in the balanced dataset augmented in this paper have improved by more than 2.37%,3.39% and 4.57%,respectively.
基金the National Natural Science Foundation of China(61761023,31760315)the Natural Science Foundation of Jiangxi Province,China(20202BABL202004,20202BAB202007)the Scientific Research Plan of the Department of Education of Jiangxi Province(GJJ190695)。
文摘As one important type of post-translational modifications(PTMs),protein lysine succinylation regulates many important biological processes.It is also closely involved with some major diseases in the aspects of Cardiometabolic,liver metabolic,nervous system and so on.Therefore,it is imperative to predict the succinylation sites in proteins for both basic research and drug development.In this paper,a novel predictor called i Succ Lys-BLS was proposed by not only introducing a new machine learning algorithm—Broad Learning System,but also optimizing the imbalanced data by randomly labeling samples.Rigorous cross-validation and independent test indicate that the success rate of i Succ Lys-BLS for positive samples is overwhelmingly higher than its counterparts.
基金primarily supported by the Ministry of Higher Education through MRUN Young Researchers Grant Scheme(MY-RGS),MR001-2019,entitled“Climate Change Mitigation:Artificial Intelligence-Based Integrated Environmental System for Mangrove Forest Conservation,”received by K.H.,S.A.R.,H.F.H.,M.I.M.,and M.M.Asecondarily funded by the UM-RU Grant,ST065-2021,entitled Climate Smart Mitigation and Adaptation:Integrated Climate Resilience Strategy for Tropical Marine Ecosystem.
文摘A common difficulty in building prediction models with real-world environmental datasets is the skewed distribution of classes.There are significantly more samples for day-to-day classes,while rare events such as polluted classes are uncommon.Consequently,the limited availability of minority outcomes lowers the classifier’s overall reliability.This study assesses the capability of machine learning(ML)algorithms in tackling imbalanced water quality data based on the metrics of precision,recall,and F1 score.It intends to balance the misled accuracy towards the majority of data.Hence,10 ML algorithms of its performance are compared.The classifiers included are AdaBoost,SupportVector Machine,Linear Discriminant Analysis,k-Nearest Neighbors,Naive Bayes,Decision Trees,Random Forest,Extra Trees,Bagging,and the Multilayer Perceptron.This study also uses the Easy Ensemble Classifier,Balanced Bagging,andRUSBoost algorithm to evaluatemulti-class imbalanced learning methods.The comparison results revealed that a highaccuracy machine learning model is not always good in recall and sensitivity.This paper’s stacked ensemble deep learning(SE-DL)generalization model effectively classifies the water quality index(WQI)based on 23 input variables.The proposed algorithm achieved a remarkable average of 95.69%,94.96%,92.92%,and 93.88%for accuracy,precision,recall,and F1 score,respectively.In addition,the proposed model is compared against two state-of-the-art classifiers,the XGBoost(eXtreme Gradient Boosting)and Light Gradient Boosting Machine,where performance metrics of balanced accuracy and g-mean are included.The experimental setup concluded XGBoost with a higher balanced accuracy and G-mean.However,the SE-DL model has a better and more balanced performance in the F1 score.The SE-DL model aligns with the goal of this study to ensure the balance between accuracy and completeness for each water quality class.The proposed algorithm is also capable of higher efficiency at a lower computational time against using the standard SyntheticMinority Oversampling Technique(SMOTE)approach to imbalanced datasets.
文摘Purpose: This paper aims to improve the classification performance when the data is imbalanced by applying different sampling techniques available in Machine Learning.Design/methodology/approach: The medical appointment no-show dataset is imbalanced, and when classification algorithms are applied directly to the dataset, it is biased towards the majority class, ignoring the minority class. To avoid this issue, multiple sampling techniques such as Random Over Sampling(ROS), Random Under Sampling(RUS), Synthetic Minority Oversampling TEchnique(SMOTE), ADAptive SYNthetic Sampling(ADASYN), Edited Nearest Neighbor(ENN), and Condensed Nearest Neighbor(CNN) are applied in order to make the dataset balanced. The performance is assessed by the Decision Tree classifier with the listed sampling techniques and the best performance is identified.Findings: This study focuses on the comparison of the performance metrics of various sampling methods widely used. It is revealed that, compared to other techniques, the Recall is high when ENN is applied CNN and ADASYN have performed equally well on the Imbalanced data.Research limitations: The testing was carried out with limited dataset and needs to be tested with a larger dataset.Practical implications: This framework will be useful whenever the data is imbalanced in real world scenarios, which ultimately improves the performance.Originality/value: This paper uses the rebalancing framework on medical appointment no-show dataset to predict the no-shows and removes the bias towards minority class.
基金National Natural Science Foundation of China,Grant/Award Number:61972261Basic Research Foundations of Shenzhen,Grant/Award Numbers:JCYJ20210324093609026,JCYJ20200813091134001。
文摘In this paper,an Observation Points Classifier Ensemble(OPCE)algorithm is proposed to deal with High-Dimensional Imbalanced Classification(HDIC)problems based on data processed using the Multi-Dimensional Scaling(MDS)feature extraction technique.First,dimensionality of the original imbalanced data is reduced using MDS so that distances between any two different samples are preserved as well as possible.Second,a novel OPCE algorithm is applied to classify imbalanced samples by placing optimised observation points in a low-dimensional data space.Third,optimization of the observation point mappings is carried out to obtain a reliable assessment of the unknown samples.Exhaustive experiments have been conducted to evaluate the feasibility,rationality,and effectiveness of the proposed OPCE algorithm using seven benchmark HDIC data sets.Experimental results show that(1)the OPCE algorithm can be trained faster on low-dimensional imbalanced data than on high-dimensional data;(2)the OPCE algorithm can correctly identify samples as the number of optimised observation points is increased;and(3)statistical analysis reveals that OPCE yields better HDIC performances on the selected data sets in comparison with eight other HDIC algorithms.This demonstrates that OPCE is a viable algorithm to deal with HDIC problems.
文摘Diabetics is one of the world’s most common diseases which are caused by continued high levels of blood sugar.The risk of diabetics can be lowered if the diabetic is found at the early stage.In recent days,several machine learning models were developed to predict the diabetic presence at an early stage.In this paper,we propose an embedded-based machine learning model that combines the split-vote method and instance duplication to leverage an imbalanced dataset called PIMA Indian to increase the prediction of diabetics.The proposed method uses both the concept of over-sampling and under-sampling along with model weighting to increase the performance of classification.Different measures such as Accuracy,Precision,Recall,and F1-Score are used to evaluate the model.The results we obtained using K-Nearest Neighbor(kNN),Naïve Bayes(NB),Support Vector Machines(SVM),Random Forest(RF),Logistic Regression(LR),and Decision Trees(DT)were 89.32%,91.44%,95.78%,89.3%,81.76%,and 80.38%respectively.The SVM model is more efficient than other models which are 21.38%more than exiting machine learning-based works.
基金supported by National Natural Science Foundation of China(Grant Nos.52279137,52009090).
文摘Estimation of construction parameters is crucial for optimizing tunnel construction schedule.Due to the influence of routine activities and occasional risk events,these parameters are usually correlated and imbalanced.To solve this issue,an improved bidirectional generative adversarial network(BiGAN)model with a joint discriminator structure and zero-centered gradient penalty(0-GP)is proposed.In this model,in order to improve the capability of original BiGAN in learning imbalanced parameters,the joint discriminator separately discriminates the routine activities and risk event durations to balance their influence weights.Then,the self-attention mechanism is embedded so that the discriminator can pay more attention to the imbalanced parameters.Finally,the 0-GP is adapted for the loss of the discrimi-nator to improve its convergence and stability.A case study of a tunnel in China shows that the improved BiGAN can obtain parameter estimates consistent with the classical Gauss mixture model,without the need of tedious and complex correlation analysis.The proposed joint discriminator can increase the ability of BiGAN in estimating imbalanced construction parameters,and the 0-GP can ensure the stability and convergence of the model.
文摘Most modern technologies,such as social media,smart cities,and the internet of things(IoT),rely on big data.When big data is used in the real-world applications,two data challenges such as class overlap and class imbalance arises.When dealing with large datasets,most traditional classifiers are stuck in the local optimum problem.As a result,it’s necessary to look into new methods for dealing with large data collections.Several solutions have been proposed for overcoming this issue.The rapid growth of the available data threatens to limit the usefulness of many traditional methods.Methods such as oversampling and undersampling have shown great promises in addressing the issues of class imbalance.Among all of these techniques,Synthetic Minority Oversampling TechniquE(SMOTE)has produced the best results by generating synthetic samples for the minority class in creating a balanced dataset.The issue is that their practical applicability is restricted to problems involving tens of thousands or lower instances of each.In this paper,we have proposed a parallel mode method using SMOTE and MapReduce strategy,this distributes the operation of the algorithm among a group of computational nodes for addressing the aforementioned problem.Our proposed solution has been divided into three stages.Thefirst stage involves the process of splitting the data into different blocks using a mapping function,followed by a pre-processing step for each mapping block that employs a hybrid SMOTE algo-rithm for solving the class imbalanced problem.On each map block,a decision tree model would be constructed.Finally,the decision tree blocks would be com-bined for creating a classification model.We have used numerous datasets with up to 4 million instances in our experiments for testing the proposed scheme’s cap-abilities.As a result,the Hybrid SMOTE appears to have good scalability within the framework proposed,and it also cuts down the processing time.
基金supported by the National Key R&D Program of China,No.2022YFC3006302.
文摘Imbalance is a distinctive feature of many datasets,and how to make the dataset balanced become a hot topic in the machine learning field.The Synthetic Minority Oversampling Technique(SMOTE)is the classical method to solve this problem.Although much research has been conducted on SMOTE,there is still the problem of synthetic sample singularity.To solve the issues of class imbalance and diversity of generated samples,this paper proposes a hybrid resampling method for binary imbalanced data sets,RE-SMOTE,which is designed based on the improvements of two oversampling methods parameter-free SMOTE(PF-SMOTE)and SMOTE-Weighted Ensemble Nearest Neighbor(SMOTE-WENN).Initially,minority class samples are divided into safe and boundary minority categories.Boundary minority samples are regenerated through linear interpolation with the nearest majority class samples.In contrast,safe minority samples are randomly generated within a circular range centered on the initial safe minority samples with a radius determined by the distance to the nearest majority class samples.Furthermore,we use Weighted Edited Nearest Neighbor(WENN)and relative density methods to clean the generated samples and remove the low-quality samples.Relative density is calculated based on the ratio of majority to minority samples among the reverse k-nearest neighbor samples.To verify the effectiveness and robustness of the proposed model,we conducted a comprehensive experimental study on 40 datasets selected from real applications.The experimental results show the superiority of radius estimation-SMOTE(RE-SMOTE)over other state-of-the-art methods.Code is available at:https://github.com/blue9792/RE-SMOTE(accessed on 30 September 2024).
