An Ensemble Tree Classifier for Highly Imbalanced Data Classification

作     者：SHI Peibei WANG Zhong SHI Peibei;WANG Zhong

作者机构：School of Computer Science and TechnologyHefei Normal UniversityHefei 230601China 

出 版 物：《Journal of Systems Science & Complexity》 (系统科学与复杂性学报（英文版）)

年 卷 期：2021年第34卷第6期

页      面：2250-2266页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Natural Science Foundation of China under Grant No.61976198 the Natural Science Research Key Project for Colleges and Universities of Anhui Province under Grant No.KJ2019A0726 the High-level Scientific Research Foundation for the Introduction of Talent of Hefei Normal University under Grant No.2020RCJJ44 

主　　题：Ensemble learning F-measure imbalanced classification mathematical model 

摘      要：The performance of traditional imbalanced classification algorithms is degraded when dealing with highly imbalanced *** to deal with highly imbalanced data is a difficult *** this paper,the authors propose an ensemble tree classifier for highly imbalanced data *** ensemble tree classifier is constructed with a complete binary tree structure.A mathematical model is established based on the features and classification performance of the classifier,and it is proven that the model parameters of the ensemble classifier can be solved by ***,the AdaBoost method is used as the benchmark classifier to construct the tree structure ***,the classification cost of the model is calculated,and the quantitative mathematical description between the cost and features of the ensemble tree classifier model is ***,the cost of the classification model is transformed into an optimization problem,and the parameters of the integrated tree classifier are given through theoretical *** approach is tested on several highly imbalanced datasets in different fields and takes the AUC(area under the curve)and F-measure as evaluation *** with the traditional imbalanced classification algorithm,the ensemble tree classifier has better classification performance.