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
Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services...Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.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
To address the confrontation decision-making issues in multi-round air combat,a dynamic game decision method is proposed based on decision tree for the confrontation of unmanned aerial vehicle(UAV)air combat.Based on ...To address the confrontation decision-making issues in multi-round air combat,a dynamic game decision method is proposed based on decision tree for the confrontation of unmanned aerial vehicle(UAV)air combat.Based on game the-ory and the confrontation characteristics of air combat,a dynamic game process is constructed including the strategy sets,the situation information,and the maneuver decisions for both sides of air combat.By analyzing the UAV’s flight dyna-mics and the both sides’information,a payment matrix is estab-lished through the situation advantage function,performance advantage function,and profit function.Furthermore,the dynamic game decision problem is solved based on the linear induction method to obtain the Nash equilibrium solution,where the decision tree method is introduced to obtain the optimal maneuver decision,thereby improving the situation advantage in the next round of confrontation.According to the analysis,the simulation results for the confrontation scenarios of multi-round air combat are presented to verify the effectiveness and advan-tages of the proposed method.展开更多
System upgrades in unmanned systems have made Unmanned Aerial Vehicle(UAV)-based patrolling and monitoring a preferred solution for ocean surveillance.However,dynamic environments and large-scale deployments pose sign...System upgrades in unmanned systems have made Unmanned Aerial Vehicle(UAV)-based patrolling and monitoring a preferred solution for ocean surveillance.However,dynamic environments and large-scale deployments pose significant challenges for efficient decision-making,necessitating a modular multiagent control system.Deep Reinforcement Learning(DRL)and Decision Tree(DT)have been utilized for these complex decision-making tasks,but each has its limitations:DRL is highly adaptive but lacks interpretability,while DT is inherently interpretable but has limited adaptability.To overcome these challenges,we propose the Adaptive Interpretable Decision Tree(AIDT),an evolutionary-based algorithm that is both adaptable to diverse environmental settings and highly interpretable in its decision-making processes.We first construct a Markov decision process(MDP)-based simulation environment using the Cooperative Submarine Search task as a representative scenario for training and testing the proposed method.Specifically,we use the heat map as a state variable to address the issue of multi-agent input state proliferation.Next,we introduce the curiosity-guiding intrinsic reward to encourage comprehensive exploration and enhance algorithm performance.Additionally,we incorporate decision tree size as an influence factor in the adaptation process to balance task completion with computational efficiency.To further improve the generalization capability of the decision tree,we apply a normalization method to ensure consistent processing of input states.Finally,we validate the proposed algorithm in different environmental settings,and the results demonstrate both its adaptability and interpretability.展开更多
This study investigates the use of a decision tree classification model, combined with Principal Component Analysis (PCA), to distinguish between Assam and Bhutan ethnic groups based on specific anthropometric feature...This study investigates the use of a decision tree classification model, combined with Principal Component Analysis (PCA), to distinguish between Assam and Bhutan ethnic groups based on specific anthropometric features, including age, height, tail length, hair length, bang length, reach, and earlobe type. The dataset was reduced using PCA, which identified height, reach, and age as key features contributing to variance. However, while PCA effectively reduced dimensionality, it faced challenges in clearly distinguishing between the two ethnic groups, a limitation noted in previous research. In contrast, the decision tree model performed significantly better, establishing clear decision boundaries and achieving high classification accuracy. The decision tree consistently selected Height and Reach as the most important classifiers, a finding supported by existing studies on ethnic differences in Northeast India. The results highlight the strengths of combining PCA for dimensionality reduction with decision tree models for classification tasks. While PCA alone was insufficient for optimal class separation, its integration with decision trees improved both the model’s accuracy and interpretability. Future research could explore other machine learning models to enhance classification and examine a broader set of anthropometric features for more comprehensive ethnic group classification.展开更多
[Objective] The aim was to explore the feasibility of using single spectrum image to classify crops based on multi-spectral image and Decision Tree Method. [Method] Taking the typical agriculture plantation area in Hu...[Objective] The aim was to explore the feasibility of using single spectrum image to classify crops based on multi-spectral image and Decision Tree Method. [Method] Taking the typical agriculture plantation area in Hulunbeier area, according to field measured spectrum data, the optimum time of main crops, barley, wheat, rapeseed, based on crops spectrum characteristics, by dint of decision-making tree method, and considering spectral matching method, classification of crops was studied such as SAM. [Result] By dint of Landsat TM image gained in the first half of August, based on geographic and atmospheric proof-reading, decision-making tree was constructed. Plantation information about wheat, barley, and rapeseed and plantation grassland was extracted successfully. The general classification accuracy reached 86.90%. Kappa coefficient was 0.831 1. [Conclusion] Taking typical spectrum image as data source, and applying Decision Tree Method to get crops type＇s information had fine application future.展开更多
Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for st...Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.展开更多
Structural damage detection(SDD)remains highly challenging,due to the difficulty in selecting the optimal damage features from a vast amount of information.In this study,a tree model-based method using decision tree a...Structural damage detection(SDD)remains highly challenging,due to the difficulty in selecting the optimal damage features from a vast amount of information.In this study,a tree model-based method using decision tree and random forest was employed for feature selection of vibration response signals in SDD.Signal datasets were obtained by numerical experiments and vibration experiments,respectively.Dataset features extracted using this method were input into a convolutional neural network to determine the location of structural damage.Results indicated a 5%to 10%improvement in detection accuracy compared to using original datasets without feature selection,demonstrating the feasibility of this method.The proposed method,based on tree model and classification,addresses the issue of extracting effective information from numerous vibration response signals in structural health monitoring.展开更多
Database watermarking technologies provide an effective solution to data security problems by embedding the watermark in the database to prove copyright or trace the source of data leakage.However,when the watermarked...Database watermarking technologies provide an effective solution to data security problems by embedding the watermark in the database to prove copyright or trace the source of data leakage.However,when the watermarked database is used for data mining model building,such as decision trees,it may cause a different mining result in comparison with the result from the original database caused by the distortion of watermark embedding.Traditional watermarking algorithms mainly consider the statistical distortion of data,such as the mean square error,but very few consider the effect of the watermark on database mining.Therefore,in this paper,a consistency preserving database watermarking algorithm is proposed for decision trees.First,label classification statistics and label state transfer methods are proposed to adjust the watermarked data so that the model structure of the watermarked decision tree is the same as that of the original decision tree.Then,the splitting values of the decision tree are adjusted according to the defined constraint equations.Finally,the adjusted database can obtain a decision tree consistent with the original decision tree.The experimental results demonstrated that the proposed algorithm does not corrupt the watermarks,and makes the watermarked decision tree consistent with the original decision tree with a small distortion.展开更多
To solve the multi-class fault diagnosis tasks, decision tree support vector machine （DTSVM）, which combines SVM and decision tree using the concept of dichotomy, is proposed. Since the classification performance of...To solve the multi-class fault diagnosis tasks, decision tree support vector machine （DTSVM）, which combines SVM and decision tree using the concept of dichotomy, is proposed. Since the classification performance of DTSVM highly depends on its structure, to cluster the multi-classes with maximum distance between the clustering centers of the two sub-classes, genetic algorithm is introduced into the formation of decision tree, so that the most separable classes would be separated at each node of decisions tree. Numerical simulations conducted on three datasets compared with ＂one-against-all＂ and ＂one-against-one＂ demonstrate the proposed method has better performance and higher generalization ability than the two conventional methods.展开更多
With the development of the service-oriented computing(SOC),web service has an important and popular solution for the design of the application system to various enterprises.Nowadays,the numerous web services are prov...With the development of the service-oriented computing(SOC),web service has an important and popular solution for the design of the application system to various enterprises.Nowadays,the numerous web services are provided by the service providers on the network,it becomes difficult for users to select the best reliable one from a large number of services with the same function.So it is necessary to design feasible selection strategies to provide users with the reliable services.Most existing methods attempt to select services according to accurate predictions for the quality of service(QoS)values.However,because the network and user needs are dynamic,it is almost impossible to accurately predict the QoS values.Furthermore,accurate prediction is generally time-consuming.This paper proposes a service decision tree based post-pruning prediction approach.This paper first defines the five reliability levels for measuring the reliability of services.By analyzing the quality data of service from the network,the proposed method can generate the training set and convert them into the service decision tree model.Using the generated model and the given predicted services,the proposed method classifies the service to the corresponding reliability level after discretizing the continuous attribute of service.Moreover,this paper applies the post-pruning strategy to optimize the generated model for avoiding the over-fitting.Experimental results show that the proposed method is effective in predicting the service reliability.展开更多
Accurate classification of cardiac arrhythmias is a crucial task because of the non-stationary nature of electrocardiogram(ECG)signals.In a life-threatening situation,an automated system is necessary for early detecti...Accurate classification of cardiac arrhythmias is a crucial task because of the non-stationary nature of electrocardiogram(ECG)signals.In a life-threatening situation,an automated system is necessary for early detection of beat abnormalities in order to reduce the mortality rate.In this paper,we propose an automatic classification system of ECG beats based on the multi-domain features derived from the ECG signals.The experimental study was evaluated on ECG signals obtained from the MIT-BIH Arrhythmia Database.The feature set comprises eight empirical mode decomposition(EMD)based features,three features from variational mode decomposition(VMD)and four features from RR intervals.In total,15 features are ranked according to a ranker search approach and then used as input to the support vector machine(SVM)and C4.5 decision tree classifiers for classifying six types of arrhythmia beats.The proposed method achieved best result in C4.5 decision tree classifier with an accuracy of 98.89%compared to cubic-SVM classifier which achieved an accuracy of 95.35%only.Besides accuracy measures,all other parameters such as sensitivity(Se),specificity(Sp)and precision rates of 95.68%,99.28%and 95.8%was achieved better in C4.5 classifier.Also the computational time of 0.65 s with an error rate of 0.11 was achieved which is very less compared to SVM.The multi-domain based features with decision tree classifier obtained the best results in classifying cardiac arrhythmias hence the system could be used efficiently in clinical practices.展开更多
The North China Plain and the agricultural region are crossed by the Shanxi-Beijing natural gas pipeline.Resi-dents in the area use rototillers for planting and harvesting;however,the depth of the rototillers into the...The North China Plain and the agricultural region are crossed by the Shanxi-Beijing natural gas pipeline.Resi-dents in the area use rototillers for planting and harvesting;however,the depth of the rototillers into the ground is greater than the depth of the pipeline,posing a significant threat to the safe operation of the pipeline.Therefore,it is of great significance to study the dynamic response of rotary tillers impacting pipelines to ensure the safe opera-tion of pipelines.This article focuses on the Shanxi-Beijing natural gas pipeline,utilizingfinite element simulation software to establish afinite element model for the interaction among the machinery,pipeline,and soil,and ana-lyzing the dynamic response of the pipeline.At the same time,a decision tree model is introduced to classify the damage of pipelines under different working conditions,and the boundary value and importance of each influen-cing factor on pipeline damage are derived.Considering the actual conditions in the hemp yam planting area,targeted management measures have been proposed to ensure the operational safety of the Shanxi-Beijing natural gas pipeline in this region.展开更多
Machine learning has emerged as a pivotal tool in deciphering and managing this excess of information in an era of abundant data.This paper presents a comprehensive analysis of machine learning algorithms,focusing on ...Machine learning has emerged as a pivotal tool in deciphering and managing this excess of information in an era of abundant data.This paper presents a comprehensive analysis of machine learning algorithms,focusing on the structure and efficacy of random forests in mitigating overfitting—a prevalent issue in decision tree models.It also introduces a novel approach to enhancing decision tree performance through an optimized pruning method called Adaptive Cross-Validated Alpha CCP(ACV-CCP).This method refines traditional cost complexity pruning by streamlining the selection of the alpha parameter,leveraging cross-validation within the pruning process to achieve a reliable,computationally efficient alpha selection that generalizes well to unseen data.By enhancing computational efficiency and balancing model complexity,ACV-CCP allows decision trees to maintain predictive accuracy while minimizing overfitting,effectively narrowing the performance gap between decision trees and random forests.Our findings illustrate how ACV-CCP contributes to the robustness and applicability of decision trees,providing a valuable perspective on achieving computationally efficient and generalized machine learning models.展开更多
Karst rocky desertification is a phenomenon of land degradation as a result of affection by the interaction of natural and human factors.In the past,in the rocky desertification areas,supervised classification and uns...Karst rocky desertification is a phenomenon of land degradation as a result of affection by the interaction of natural and human factors.In the past,in the rocky desertification areas,supervised classification and unsupervised classification are often used to classify the remote sensing image.But they only use pixel brightness characteristics to classify it.So the classification accuracy is low and can not meet the needs of practical application.Decision tree classification is a new technology for remote sensing image classification.In this study,we select the rocky desertification areas Kaizuo Township as a case study,use the ASTER image data,DEM and lithology data,by extracting the normalized difference vegetation index,ratio vegetation index,terrain slope and other data to establish classification rules to build decision trees.In the ENVI software support,we access the classification images.By calculating the classification accuracy and kappa coefficient,we find that better classification results can be obtained,desertification information can be extracted automatically and if more remote sensing image bands used,higher resolution DEM employed and less errors data reduced during processing,classification accuracy can be improve further.展开更多
The trend toward designing an intelligent distribution system based on students’individual differences and individual needs has taken precedence in view of the traditional dormitory distribution system,which neglects...The trend toward designing an intelligent distribution system based on students’individual differences and individual needs has taken precedence in view of the traditional dormitory distribution system,which neglects the students’personality traits,causes dormitory disputes,and affects the students’quality of life and academic quality.This paper collects freshmen's data according to college students’personal preferences,conducts a classification comparison,uses the decision tree classification algorithm based on the information gain principle as the core algorithm of dormitory allocation,determines the description rules of students’personal preferences and decision tree classification preferences,completes the conceptual design of the database of entity relations and data dictionaries,meets students’personality classification requirements for the dormitory,and lays the foundation for the intelligent dormitory allocation system.展开更多
Decision tree is an effective supervised learning method for solving classification and regression problems. This article combines the Pearson correlation coefficient with the CART decision tree, replacing the Gini co...Decision tree is an effective supervised learning method for solving classification and regression problems. This article combines the Pearson correlation coefficient with the CART decision tree, replacing the Gini coefficient with the correlation coefficient to consider the correlation between conditional attributes, prioritizing the selection of conditional attributes with higher correlation coefficients as leaf nodes. The collected data on homebuyers is divided into age groups, including youth, middle-aged, and elderly groups. Both traditional CART decision tree and improved CART decision tree are applied to this problem, and after comparison, it is found that the depth of the CART decision tree in this study is reduced, the number of leaf nodes is decreased, the time complexity is shortened, efficiency is improved, and pruning issues are avoided. Finally, corresponding housing recommendations are given to homebuyers of different ages.展开更多
Flood disasters can have a serious impact on people's production and lives, and can cause hugelosses in lives and property security. Based on multi-source remote sensing data, this study establisheddecision tree c...Flood disasters can have a serious impact on people's production and lives, and can cause hugelosses in lives and property security. Based on multi-source remote sensing data, this study establisheddecision tree classification rules through multi-source and multi-temporal feature fusion, classified groundobjects before the disaster and extracted flood information in the disaster area based on optical imagesduring the disaster, so as to achieve rapid acquisition of the disaster situation of each disaster bearing object.In the case of Qianliang Lake, which suffered from flooding in 2020, the results show that decision treeclassification algorithms based on multi-temporal features can effectively integrate multi-temporal and multispectralinformation to overcome the shortcomings of single-temporal image classification and achieveground-truth object classification.展开更多
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.
文摘Urban tree species provide various essential ecosystem services in cities,such as regulating urban temperatures,reducing noise,capturing carbon,and mitigating the urban heat island effect.The quality of these services is influenced by species diversity,tree health,and the distribution and the composition of trees.Traditionally,data on urban trees has been collected through field surveys and manual interpretation of remote sensing images.In this study,we evaluated the effectiveness of multispectral airborne laser scanning(ALS)data in classifying 24 common urban roadside tree species in Espoo,Finland.Tree crown structure information,intensity features,and spectral data were used for classification.Eight different machine learning algorithms were tested,with the extra trees(ET)algorithm performing the best,achieving an overall accuracy of 71.7%using multispectral LiDAR data.This result highlights that integrating structural and spectral information within a single framework can improve the classification accuracy.Future research will focus on identifying the most important features for species classification and developing algorithms with greater efficiency and accuracy.
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.
基金supported by the Major Projects for Science and Technology Innovation 2030(2018AAA0100805).
文摘To address the confrontation decision-making issues in multi-round air combat,a dynamic game decision method is proposed based on decision tree for the confrontation of unmanned aerial vehicle(UAV)air combat.Based on game the-ory and the confrontation characteristics of air combat,a dynamic game process is constructed including the strategy sets,the situation information,and the maneuver decisions for both sides of air combat.By analyzing the UAV’s flight dyna-mics and the both sides’information,a payment matrix is estab-lished through the situation advantage function,performance advantage function,and profit function.Furthermore,the dynamic game decision problem is solved based on the linear induction method to obtain the Nash equilibrium solution,where the decision tree method is introduced to obtain the optimal maneuver decision,thereby improving the situation advantage in the next round of confrontation.According to the analysis,the simulation results for the confrontation scenarios of multi-round air combat are presented to verify the effectiveness and advan-tages of the proposed method.
文摘System upgrades in unmanned systems have made Unmanned Aerial Vehicle(UAV)-based patrolling and monitoring a preferred solution for ocean surveillance.However,dynamic environments and large-scale deployments pose significant challenges for efficient decision-making,necessitating a modular multiagent control system.Deep Reinforcement Learning(DRL)and Decision Tree(DT)have been utilized for these complex decision-making tasks,but each has its limitations:DRL is highly adaptive but lacks interpretability,while DT is inherently interpretable but has limited adaptability.To overcome these challenges,we propose the Adaptive Interpretable Decision Tree(AIDT),an evolutionary-based algorithm that is both adaptable to diverse environmental settings and highly interpretable in its decision-making processes.We first construct a Markov decision process(MDP)-based simulation environment using the Cooperative Submarine Search task as a representative scenario for training and testing the proposed method.Specifically,we use the heat map as a state variable to address the issue of multi-agent input state proliferation.Next,we introduce the curiosity-guiding intrinsic reward to encourage comprehensive exploration and enhance algorithm performance.Additionally,we incorporate decision tree size as an influence factor in the adaptation process to balance task completion with computational efficiency.To further improve the generalization capability of the decision tree,we apply a normalization method to ensure consistent processing of input states.Finally,we validate the proposed algorithm in different environmental settings,and the results demonstrate both its adaptability and interpretability.
文摘This study investigates the use of a decision tree classification model, combined with Principal Component Analysis (PCA), to distinguish between Assam and Bhutan ethnic groups based on specific anthropometric features, including age, height, tail length, hair length, bang length, reach, and earlobe type. The dataset was reduced using PCA, which identified height, reach, and age as key features contributing to variance. However, while PCA effectively reduced dimensionality, it faced challenges in clearly distinguishing between the two ethnic groups, a limitation noted in previous research. In contrast, the decision tree model performed significantly better, establishing clear decision boundaries and achieving high classification accuracy. The decision tree consistently selected Height and Reach as the most important classifiers, a finding supported by existing studies on ethnic differences in Northeast India. The results highlight the strengths of combining PCA for dimensionality reduction with decision tree models for classification tasks. While PCA alone was insufficient for optimal class separation, its integration with decision trees improved both the model’s accuracy and interpretability. Future research could explore other machine learning models to enhance classification and examine a broader set of anthropometric features for more comprehensive ethnic group classification.
基金Supported by the Open Subject of Key Lab of Resources Remote-sensing and Digital Agriculture in Agricultural Department(RDA1008)~~
文摘[Objective] The aim was to explore the feasibility of using single spectrum image to classify crops based on multi-spectral image and Decision Tree Method. [Method] Taking the typical agriculture plantation area in Hulunbeier area, according to field measured spectrum data, the optimum time of main crops, barley, wheat, rapeseed, based on crops spectrum characteristics, by dint of decision-making tree method, and considering spectral matching method, classification of crops was studied such as SAM. [Result] By dint of Landsat TM image gained in the first half of August, based on geographic and atmospheric proof-reading, decision-making tree was constructed. Plantation information about wheat, barley, and rapeseed and plantation grassland was extracted successfully. The general classification accuracy reached 86.90%. Kappa coefficient was 0.831 1. [Conclusion] Taking typical spectrum image as data source, and applying Decision Tree Method to get crops type＇s information had fine application future.
基金supported by the National Nat-ural Science Foundation of China(No.52203376)the National Key Research and Development Program of China(No.2023YFB3813200).
文摘Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.
基金supported by the Project of Guangdong Province High Level University Construction for Guangdong University of Technology(Grant No.262519003)the College Student Innovation Training Program of Guangdong University of Technology(Grant Nos.S202211845154 and xj2023118450384).
文摘Structural damage detection(SDD)remains highly challenging,due to the difficulty in selecting the optimal damage features from a vast amount of information.In this study,a tree model-based method using decision tree and random forest was employed for feature selection of vibration response signals in SDD.Signal datasets were obtained by numerical experiments and vibration experiments,respectively.Dataset features extracted using this method were input into a convolutional neural network to determine the location of structural damage.Results indicated a 5%to 10%improvement in detection accuracy compared to using original datasets without feature selection,demonstrating the feasibility of this method.The proposed method,based on tree model and classification,addresses the issue of extracting effective information from numerous vibration response signals in structural health monitoring.
基金supported by the National Key Research and Development Program of China under Grant 2021YFB2700600the National Natural Science Foundation of China under Grant 62132013 and 61902292+1 种基金the Key Research and Development Programs of Shaanxi under Grants 2021ZDLGY06-03the Truth-Seeking Research Scholarship Fund of Xidian University。
文摘Database watermarking technologies provide an effective solution to data security problems by embedding the watermark in the database to prove copyright or trace the source of data leakage.However,when the watermarked database is used for data mining model building,such as decision trees,it may cause a different mining result in comparison with the result from the original database caused by the distortion of watermark embedding.Traditional watermarking algorithms mainly consider the statistical distortion of data,such as the mean square error,but very few consider the effect of the watermark on database mining.Therefore,in this paper,a consistency preserving database watermarking algorithm is proposed for decision trees.First,label classification statistics and label state transfer methods are proposed to adjust the watermarked data so that the model structure of the watermarked decision tree is the same as that of the original decision tree.Then,the splitting values of the decision tree are adjusted according to the defined constraint equations.Finally,the adjusted database can obtain a decision tree consistent with the original decision tree.The experimental results demonstrated that the proposed algorithm does not corrupt the watermarks,and makes the watermarked decision tree consistent with the original decision tree with a small distortion.
基金supported by the National Natural Science Foundation of China (60604021 60874054)
文摘To solve the multi-class fault diagnosis tasks, decision tree support vector machine （DTSVM）, which combines SVM and decision tree using the concept of dichotomy, is proposed. Since the classification performance of DTSVM highly depends on its structure, to cluster the multi-classes with maximum distance between the clustering centers of the two sub-classes, genetic algorithm is introduced into the formation of decision tree, so that the most separable classes would be separated at each node of decisions tree. Numerical simulations conducted on three datasets compared with ＂one-against-all＂ and ＂one-against-one＂ demonstrate the proposed method has better performance and higher generalization ability than the two conventional methods.
基金This paper is partially supported by the National Natural Science Foundation of China under Grant No.61972053 and No.61603054by the Scientific Research Foundation of Liaoning Education Department under Grant No.LQ2019016,No.LJ2019015by the Natural Science Foundation of Liaoning Province,China under Grant No.2019-ZD-0505.
文摘With the development of the service-oriented computing(SOC),web service has an important and popular solution for the design of the application system to various enterprises.Nowadays,the numerous web services are provided by the service providers on the network,it becomes difficult for users to select the best reliable one from a large number of services with the same function.So it is necessary to design feasible selection strategies to provide users with the reliable services.Most existing methods attempt to select services according to accurate predictions for the quality of service(QoS)values.However,because the network and user needs are dynamic,it is almost impossible to accurately predict the QoS values.Furthermore,accurate prediction is generally time-consuming.This paper proposes a service decision tree based post-pruning prediction approach.This paper first defines the five reliability levels for measuring the reliability of services.By analyzing the quality data of service from the network,the proposed method can generate the training set and convert them into the service decision tree model.Using the generated model and the given predicted services,the proposed method classifies the service to the corresponding reliability level after discretizing the continuous attribute of service.Moreover,this paper applies the post-pruning strategy to optimize the generated model for avoiding the over-fitting.Experimental results show that the proposed method is effective in predicting the service reliability.
文摘Accurate classification of cardiac arrhythmias is a crucial task because of the non-stationary nature of electrocardiogram(ECG)signals.In a life-threatening situation,an automated system is necessary for early detection of beat abnormalities in order to reduce the mortality rate.In this paper,we propose an automatic classification system of ECG beats based on the multi-domain features derived from the ECG signals.The experimental study was evaluated on ECG signals obtained from the MIT-BIH Arrhythmia Database.The feature set comprises eight empirical mode decomposition(EMD)based features,three features from variational mode decomposition(VMD)and four features from RR intervals.In total,15 features are ranked according to a ranker search approach and then used as input to the support vector machine(SVM)and C4.5 decision tree classifiers for classifying six types of arrhythmia beats.The proposed method achieved best result in C4.5 decision tree classifier with an accuracy of 98.89%compared to cubic-SVM classifier which achieved an accuracy of 95.35%only.Besides accuracy measures,all other parameters such as sensitivity(Se),specificity(Sp)and precision rates of 95.68%,99.28%and 95.8%was achieved better in C4.5 classifier.Also the computational time of 0.65 s with an error rate of 0.11 was achieved which is very less compared to SVM.The multi-domain based features with decision tree classifier obtained the best results in classifying cardiac arrhythmias hence the system could be used efficiently in clinical practices.
文摘The North China Plain and the agricultural region are crossed by the Shanxi-Beijing natural gas pipeline.Resi-dents in the area use rototillers for planting and harvesting;however,the depth of the rototillers into the ground is greater than the depth of the pipeline,posing a significant threat to the safe operation of the pipeline.Therefore,it is of great significance to study the dynamic response of rotary tillers impacting pipelines to ensure the safe opera-tion of pipelines.This article focuses on the Shanxi-Beijing natural gas pipeline,utilizingfinite element simulation software to establish afinite element model for the interaction among the machinery,pipeline,and soil,and ana-lyzing the dynamic response of the pipeline.At the same time,a decision tree model is introduced to classify the damage of pipelines under different working conditions,and the boundary value and importance of each influen-cing factor on pipeline damage are derived.Considering the actual conditions in the hemp yam planting area,targeted management measures have been proposed to ensure the operational safety of the Shanxi-Beijing natural gas pipeline in this region.
文摘Machine learning has emerged as a pivotal tool in deciphering and managing this excess of information in an era of abundant data.This paper presents a comprehensive analysis of machine learning algorithms,focusing on the structure and efficacy of random forests in mitigating overfitting—a prevalent issue in decision tree models.It also introduces a novel approach to enhancing decision tree performance through an optimized pruning method called Adaptive Cross-Validated Alpha CCP(ACV-CCP).This method refines traditional cost complexity pruning by streamlining the selection of the alpha parameter,leveraging cross-validation within the pruning process to achieve a reliable,computationally efficient alpha selection that generalizes well to unseen data.By enhancing computational efficiency and balancing model complexity,ACV-CCP allows decision trees to maintain predictive accuracy while minimizing overfitting,effectively narrowing the performance gap between decision trees and random forests.Our findings illustrate how ACV-CCP contributes to the robustness and applicability of decision trees,providing a valuable perspective on achieving computationally efficient and generalized machine learning models.
文摘Karst rocky desertification is a phenomenon of land degradation as a result of affection by the interaction of natural and human factors.In the past,in the rocky desertification areas,supervised classification and unsupervised classification are often used to classify the remote sensing image.But they only use pixel brightness characteristics to classify it.So the classification accuracy is low and can not meet the needs of practical application.Decision tree classification is a new technology for remote sensing image classification.In this study,we select the rocky desertification areas Kaizuo Township as a case study,use the ASTER image data,DEM and lithology data,by extracting the normalized difference vegetation index,ratio vegetation index,terrain slope and other data to establish classification rules to build decision trees.In the ENVI software support,we access the classification images.By calculating the classification accuracy and kappa coefficient,we find that better classification results can be obtained,desertification information can be extracted automatically and if more remote sensing image bands used,higher resolution DEM employed and less errors data reduced during processing,classification accuracy can be improve further.
文摘The trend toward designing an intelligent distribution system based on students’individual differences and individual needs has taken precedence in view of the traditional dormitory distribution system,which neglects the students’personality traits,causes dormitory disputes,and affects the students’quality of life and academic quality.This paper collects freshmen's data according to college students’personal preferences,conducts a classification comparison,uses the decision tree classification algorithm based on the information gain principle as the core algorithm of dormitory allocation,determines the description rules of students’personal preferences and decision tree classification preferences,completes the conceptual design of the database of entity relations and data dictionaries,meets students’personality classification requirements for the dormitory,and lays the foundation for the intelligent dormitory allocation system.
文摘Decision tree is an effective supervised learning method for solving classification and regression problems. This article combines the Pearson correlation coefficient with the CART decision tree, replacing the Gini coefficient with the correlation coefficient to consider the correlation between conditional attributes, prioritizing the selection of conditional attributes with higher correlation coefficients as leaf nodes. The collected data on homebuyers is divided into age groups, including youth, middle-aged, and elderly groups. Both traditional CART decision tree and improved CART decision tree are applied to this problem, and after comparison, it is found that the depth of the CART decision tree in this study is reduced, the number of leaf nodes is decreased, the time complexity is shortened, efficiency is improved, and pruning issues are avoided. Finally, corresponding housing recommendations are given to homebuyers of different ages.
文摘Flood disasters can have a serious impact on people's production and lives, and can cause hugelosses in lives and property security. Based on multi-source remote sensing data, this study establisheddecision tree classification rules through multi-source and multi-temporal feature fusion, classified groundobjects before the disaster and extracted flood information in the disaster area based on optical imagesduring the disaster, so as to achieve rapid acquisition of the disaster situation of each disaster bearing object.In the case of Qianliang Lake, which suffered from flooding in 2020, the results show that decision treeclassification algorithms based on multi-temporal features can effectively integrate multi-temporal and multispectralinformation to overcome the shortcomings of single-temporal image classification and achieveground-truth object classification.
