A Novel Neighborhood-Weighted Sampling Method for Imbalanced Datasets

作     者：GUANG Mingjian YAN Chungang LIU Guanjun WANG Junli JIANG Changjun GUANG Mingjian;YAN Chungang;LIU Guanjun;WANG Junli;JIANG Changjun

作者机构：Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2022年第31卷第5期

页      面：969-979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 Key Research and Development Program of China (2018YFB2100801) 

主　　题：parameter reduction method neighborhood-weighted sampling method pattern classification class imbalance problem NWBBagging k-nearest neighbors sampling methods imbalanced datasets nearest neighbour methods ensemble learning algorithm Bagging algorithm 

摘      要：The weighted sampling methods based on k-nearest neighbors have been demonstrated to be effective in solving the class imbalance problem. However,they usually ignore the positional relationship between a sample and the heterogeneous samples in its neighborhood when calculating sample weight. This paper proposes a novel neighborhood-weighted based sampling method named NWBBagging to improve the Bagging algorithm s performance on imbalanced datasets. It considers the positional relationship between the center sample and the heterogeneous samples in its neighborhood when identifying critical samples. And a parameter reduction method is proposed and combined into the ensemble learning framework, which reduces the parameters and increases the classifier s diversity. We compare NWBBagging with some state-of-the-art ensemble learning algorithms on 34 imbalanced datasets, and the result shows that NWBBagging achieves better performance.