The increase of competition, economic recession and financial crises has increased business failure and depending on this the researchers have attempted to develop new approaches which can yield more correct and more ...The increase of competition, economic recession and financial crises has increased business failure and depending on this the researchers have attempted to develop new approaches which can yield more correct and more reliable results. The classification and regression tree （CART） is one of the new modeling techniques which is developed for this purpose. In this study, the classification and regression trees method is explained and tested the power of the financial failure prediction. CART is applied for the data of industry companies which is trade in Istanbul Stock Exchange （ISE） between 1997-2007. As a result of this study, it has been observed that, CART has a high predicting power of financial failure one, two and three years prior to failure, and profitability ratios being the most important ratios in the prediction of failure.展开更多
According to groundwater level monitoring data of Shuping landslide in the Three Gorges Reservoir area, based on the response relationship between influential factors such as rainfall and reservoir level and the chang...According to groundwater level monitoring data of Shuping landslide in the Three Gorges Reservoir area, based on the response relationship between influential factors such as rainfall and reservoir level and the change of groundwater level, the influential factors of groundwater level were selected. Then the classification and regression tree（CART） model was constructed by the subset and used to predict the groundwater level. Through the verification, the predictive results of the test sample were consistent with the actually measured values, and the mean absolute error and relative error is 0.28 m and 1.15%respectively. To compare the support vector machine（SVM） model constructed using the same set of factors, the mean absolute error and relative error of predicted results is 1.53 m and 6.11% respectively. It is indicated that CART model has not only better fitting and generalization ability, but also strong advantages in the analysis of landslide groundwater dynamic characteristics and the screening of important variables. It is an effective method for prediction of ground water level in landslides.展开更多
The feasibility of constructing shallow foundations on saturated sands remains uncertain.Seismic design standards simply stipulate that geotechnical investigations for a shallow foundation on such soils shall be condu...The feasibility of constructing shallow foundations on saturated sands remains uncertain.Seismic design standards simply stipulate that geotechnical investigations for a shallow foundation on such soils shall be conducted to mitigate the effects of the liquefaction hazard.This study investigates the seismic behavior of strip foundations on typical two-layered soil profiles-a natural loose sand layer supported by a dense sand layer.Coupled nonlinear dynamic analyses have been conducted to calculate response parameters,including seismic settlement,the acceleration response on the ground surface,and excess pore pressure beneath strip foundations.A novel liquefaction potential index(LPI_(footing)),based on excess pore pressure ratios across a given region of soil mass beneath footings is introduced to classify liquefaction severity into three distinct levels:minor,moderate,and severe.To validate the proposed LPI_(footing),the foundation settlement is evaluated for the different liquefaction potential classes.A classification tree model has been grown to predict liquefaction susceptibility,utilizing various input variables,including earthquake intensity on the ground surface,foundation pressure,sand permeability,and top layer thickness.Moreover,a nonlinear regression function has been established to map LPI_(footing) in relation to these input predictors.The models have been constructed using a substantial dataset comprising 13,824 excess pore pressure ratio time histories.The performance of the developed models has been examined using various methods,including the 10-fold cross-validation method.The predictive capability of the tree also has been validated through existing experimental studies.The results indicate that the classification tree is not only interpretable but also highly predictive,with a testing accuracy level of 78.1%.The decision tree provides valuable insights for engineers assessing liquefaction potential beneath strip foundations.展开更多
Understanding an underlying structure for phylogenetic trees is very important as it informs on the methods that should be employed during phylogenetic inference. The methods used under a structured population differ ...Understanding an underlying structure for phylogenetic trees is very important as it informs on the methods that should be employed during phylogenetic inference. The methods used under a structured population differ from those needed when a population is not structured. In this paper, we compared two supervised machine learning techniques, that is artificial neural network (ANN) and logistic regression models for prediction of an underlying structure for phylogenetic trees. We carried out parameter tuning for the models to identify optimal models. We then performed 10-fold cross-validation on the optimal models for both logistic regression?and ANN. We also performed a non-supervised technique called clustering to identify the number of clusters that could be identified from simulated phylogenetic trees. The trees were from?both structured?and non-structured populations. Clustering and prediction using classification techniques were?done using tree statistics such as Colless, Sackin and cophenetic indices, among others. Results from 10-fold cross-validation revealed that both logistic regression and ANN models had comparable results, with both models having average accuracy rates of over 0.75. Most of the clustering indices used resulted in 2 or 3 as the optimal number of clusters.展开更多
Researchers in bioinformatics, biostatistics and other related fields seek biomarkers for many purposes, including risk assessment, disease diagnosis and prognosis, which can be formulated as a patient classification....Researchers in bioinformatics, biostatistics and other related fields seek biomarkers for many purposes, including risk assessment, disease diagnosis and prognosis, which can be formulated as a patient classification. In this paper, a new method of using a tree regression to improve logistic classification model is introduced in biomarker data analysis. The numerical results show that the linear logistic model can be significantly improved by a tree regression on the residuals. Although the classification problem of binary responses is discussed in this research, the idea is easy to extend to the classification of multinomial responses.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
In enterprise operations,maintaining manual rules for enterprise processes can be expensive,time-consuming,and dependent on specialized domain knowledge in that enterprise domain.Recently,rule-generation has been auto...In enterprise operations,maintaining manual rules for enterprise processes can be expensive,time-consuming,and dependent on specialized domain knowledge in that enterprise domain.Recently,rule-generation has been automated in enterprises,particularly through Machine Learning,to streamline routine tasks.Typically,these machine models are black boxes where the reasons for the decisions are not always transparent,and the end users need to verify the model proposals as a part of the user acceptance testing to trust it.In such scenarios,rules excel over Machine Learning models as the end-users can verify the rules and have more trust.In many scenarios,the truth label changes frequently thus,it becomes difficult for the Machine Learning model to learn till a considerable amount of data has been accumulated,but with rules,the truth can be adapted.This paper presents a novel framework for generating human-understandable rules using the Classification and Regression Tree(CART)decision tree method,which ensures both optimization and user trust in automated decision-making processes.The framework generates comprehensible rules in the form of if condition and then predicts class even in domains where noise is present.The proposed system transforms enterprise operations by automating the production of human-readable rules from structured data,resulting in increased efficiency and transparency.Removing the need for human rule construction saves time and money while guaranteeing that users can readily check and trust the automatic judgments of the system.The remarkable performance metrics of the framework,which achieve 99.85%accuracy and 96.30%precision,further support its efficiency in translating complex data into comprehensible rules,eventually empowering users and enhancing organizational decision-making processes.展开更多
Obstructive Sleep Apnea(OSA)is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation.The aim of ...Obstructive Sleep Apnea(OSA)is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation.The aim of this paper is to analyze the respiratory signal of a person to detect the Normal Breathing Activity and the Sleep Apnea(SA)activity.In the proposed method,the time domain and frequency domain features of respiration signal obtained from the PPG device are extracted.These features are applied to the Classification and Regression Tree(CART)-Particle Swarm Optimization(PSO)classifier which classifies the signal into normal breathing signal and sleep apnea signal.The proposed method is validated to measure the performance metrics like sensitivity,specificity,accuracy and F1 score by applying time domain and frequency domain features separately.Additionally,the performance of the CART-PSO(CPSO)classification algorithm is evaluated through comparing its measures with existing classification algorithms.Concurrently,the effect of the PSO algorithm in the classifier is validated by varying the parameters of PSO.展开更多
This paper presents a supervised learning algorithm for retinal vascular segmentation based on classification and regression tree (CART) algorithm and improved adptive bosting (AdaBoost). Local binary patterns (LBP) t...This paper presents a supervised learning algorithm for retinal vascular segmentation based on classification and regression tree (CART) algorithm and improved adptive bosting (AdaBoost). Local binary patterns (LBP) texture features and local features are extracted by extracting,reversing,dilating and enhancing the green components of retinal images to construct a 17-dimensional feature vector. A dataset is constructed by using the feature vector and the data manually marked by the experts. The feature is used to generate CART binary tree for nodes,where CART binary tree is as the AdaBoost weak classifier,and AdaBoost is improved by adding some re-judgment functions to form a strong classifier. The proposed algorithm is simulated on the digital retinal images for vessel extraction (DRIVE). The experimental results show that the proposed algorithm has higher segmentation accuracy for blood vessels,and the result basically contains complete blood vessel details. Moreover,the segmented blood vessel tree has good connectivity,which basically reflects the distribution trend of blood vessels. Compared with the traditional AdaBoost classification algorithm and the support vector machine (SVM) based classification algorithm,the proposed algorithm has higher average accuracy and reliability index,which is similar to the segmentation results of the state-of-the-art segmentation algorithm.展开更多
文摘The increase of competition, economic recession and financial crises has increased business failure and depending on this the researchers have attempted to develop new approaches which can yield more correct and more reliable results. The classification and regression tree （CART） is one of the new modeling techniques which is developed for this purpose. In this study, the classification and regression trees method is explained and tested the power of the financial failure prediction. CART is applied for the data of industry companies which is trade in Istanbul Stock Exchange （ISE） between 1997-2007. As a result of this study, it has been observed that, CART has a high predicting power of financial failure one, two and three years prior to failure, and profitability ratios being the most important ratios in the prediction of failure.
基金supported by the China Earthquake Administration, Institute of Seismology Foundation (IS201526246)
文摘According to groundwater level monitoring data of Shuping landslide in the Three Gorges Reservoir area, based on the response relationship between influential factors such as rainfall and reservoir level and the change of groundwater level, the influential factors of groundwater level were selected. Then the classification and regression tree（CART） model was constructed by the subset and used to predict the groundwater level. Through the verification, the predictive results of the test sample were consistent with the actually measured values, and the mean absolute error and relative error is 0.28 m and 1.15%respectively. To compare the support vector machine（SVM） model constructed using the same set of factors, the mean absolute error and relative error of predicted results is 1.53 m and 6.11% respectively. It is indicated that CART model has not only better fitting and generalization ability, but also strong advantages in the analysis of landslide groundwater dynamic characteristics and the screening of important variables. It is an effective method for prediction of ground water level in landslides.
文摘The feasibility of constructing shallow foundations on saturated sands remains uncertain.Seismic design standards simply stipulate that geotechnical investigations for a shallow foundation on such soils shall be conducted to mitigate the effects of the liquefaction hazard.This study investigates the seismic behavior of strip foundations on typical two-layered soil profiles-a natural loose sand layer supported by a dense sand layer.Coupled nonlinear dynamic analyses have been conducted to calculate response parameters,including seismic settlement,the acceleration response on the ground surface,and excess pore pressure beneath strip foundations.A novel liquefaction potential index(LPI_(footing)),based on excess pore pressure ratios across a given region of soil mass beneath footings is introduced to classify liquefaction severity into three distinct levels:minor,moderate,and severe.To validate the proposed LPI_(footing),the foundation settlement is evaluated for the different liquefaction potential classes.A classification tree model has been grown to predict liquefaction susceptibility,utilizing various input variables,including earthquake intensity on the ground surface,foundation pressure,sand permeability,and top layer thickness.Moreover,a nonlinear regression function has been established to map LPI_(footing) in relation to these input predictors.The models have been constructed using a substantial dataset comprising 13,824 excess pore pressure ratio time histories.The performance of the developed models has been examined using various methods,including the 10-fold cross-validation method.The predictive capability of the tree also has been validated through existing experimental studies.The results indicate that the classification tree is not only interpretable but also highly predictive,with a testing accuracy level of 78.1%.The decision tree provides valuable insights for engineers assessing liquefaction potential beneath strip foundations.
文摘Understanding an underlying structure for phylogenetic trees is very important as it informs on the methods that should be employed during phylogenetic inference. The methods used under a structured population differ from those needed when a population is not structured. In this paper, we compared two supervised machine learning techniques, that is artificial neural network (ANN) and logistic regression models for prediction of an underlying structure for phylogenetic trees. We carried out parameter tuning for the models to identify optimal models. We then performed 10-fold cross-validation on the optimal models for both logistic regression?and ANN. We also performed a non-supervised technique called clustering to identify the number of clusters that could be identified from simulated phylogenetic trees. The trees were from?both structured?and non-structured populations. Clustering and prediction using classification techniques were?done using tree statistics such as Colless, Sackin and cophenetic indices, among others. Results from 10-fold cross-validation revealed that both logistic regression and ANN models had comparable results, with both models having average accuracy rates of over 0.75. Most of the clustering indices used resulted in 2 or 3 as the optimal number of clusters.
文摘Researchers in bioinformatics, biostatistics and other related fields seek biomarkers for many purposes, including risk assessment, disease diagnosis and prognosis, which can be formulated as a patient classification. In this paper, a new method of using a tree regression to improve logistic classification model is introduced in biomarker data analysis. The numerical results show that the linear logistic model can be significantly improved by a tree regression on the residuals. Although the classification problem of binary responses is discussed in this research, the idea is easy to extend to the classification of multinomial responses.
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.
文摘In enterprise operations,maintaining manual rules for enterprise processes can be expensive,time-consuming,and dependent on specialized domain knowledge in that enterprise domain.Recently,rule-generation has been automated in enterprises,particularly through Machine Learning,to streamline routine tasks.Typically,these machine models are black boxes where the reasons for the decisions are not always transparent,and the end users need to verify the model proposals as a part of the user acceptance testing to trust it.In such scenarios,rules excel over Machine Learning models as the end-users can verify the rules and have more trust.In many scenarios,the truth label changes frequently thus,it becomes difficult for the Machine Learning model to learn till a considerable amount of data has been accumulated,but with rules,the truth can be adapted.This paper presents a novel framework for generating human-understandable rules using the Classification and Regression Tree(CART)decision tree method,which ensures both optimization and user trust in automated decision-making processes.The framework generates comprehensible rules in the form of if condition and then predicts class even in domains where noise is present.The proposed system transforms enterprise operations by automating the production of human-readable rules from structured data,resulting in increased efficiency and transparency.Removing the need for human rule construction saves time and money while guaranteeing that users can readily check and trust the automatic judgments of the system.The remarkable performance metrics of the framework,which achieve 99.85%accuracy and 96.30%precision,further support its efficiency in translating complex data into comprehensible rules,eventually empowering users and enhancing organizational decision-making processes.
文摘Obstructive Sleep Apnea(OSA)is a respiratory syndrome that occurs due to insufficient airflow through the respiratory or respiratory arrest while sleeping and sometimes due to the reduced oxygen saturation.The aim of this paper is to analyze the respiratory signal of a person to detect the Normal Breathing Activity and the Sleep Apnea(SA)activity.In the proposed method,the time domain and frequency domain features of respiration signal obtained from the PPG device are extracted.These features are applied to the Classification and Regression Tree(CART)-Particle Swarm Optimization(PSO)classifier which classifies the signal into normal breathing signal and sleep apnea signal.The proposed method is validated to measure the performance metrics like sensitivity,specificity,accuracy and F1 score by applying time domain and frequency domain features separately.Additionally,the performance of the CART-PSO(CPSO)classification algorithm is evaluated through comparing its measures with existing classification algorithms.Concurrently,the effect of the PSO algorithm in the classifier is validated by varying the parameters of PSO.
基金National Natural Science Foundation of China(No.61163010)
文摘This paper presents a supervised learning algorithm for retinal vascular segmentation based on classification and regression tree (CART) algorithm and improved adptive bosting (AdaBoost). Local binary patterns (LBP) texture features and local features are extracted by extracting,reversing,dilating and enhancing the green components of retinal images to construct a 17-dimensional feature vector. A dataset is constructed by using the feature vector and the data manually marked by the experts. The feature is used to generate CART binary tree for nodes,where CART binary tree is as the AdaBoost weak classifier,and AdaBoost is improved by adding some re-judgment functions to form a strong classifier. The proposed algorithm is simulated on the digital retinal images for vessel extraction (DRIVE). The experimental results show that the proposed algorithm has higher segmentation accuracy for blood vessels,and the result basically contains complete blood vessel details. Moreover,the segmented blood vessel tree has good connectivity,which basically reflects the distribution trend of blood vessels. Compared with the traditional AdaBoost classification algorithm and the support vector machine (SVM) based classification algorithm,the proposed algorithm has higher average accuracy and reliability index,which is similar to the segmentation results of the state-of-the-art segmentation algorithm.
