文献类型：期刊文献

英文题名：Boundary-Eliminated Pseudoinverse Linear Discriminant for Imbalanced Problems

作者：Zhu, Yujin[1];Wang, Zhe[1];Zha, Hongyuan[2];Gao, Daqi[1]

机构：[1]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[2]Georgia Inst Technol, Coll Comp, Sch Computat Sci & Engn, Atlanta, GA 30332 USA

年份：2018

卷号：29

期号：6

起止页码：2581

外文期刊名：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录：;EI(收录号：20172203708677);WOS:【SCI-EXPANDED(收录号:WOS:000432398300043)】；

基金：This work was supported in part by the Natural Science Foundations of China under Grant 61672227 and in part by the 863 Plan of China Ministry of Science and Technology under Grant 2015AA020107.

语种：英文

外文关键词：Heuristic method; imbalanced data; k-nearest neighbor (kNN) rule; pattern classification; pseudoinverse linear discriminant (PILD)

摘要：Existing learning models for classification of imbalanced data sets can be grouped as either boundary-based or nonboundary-based depending on whether a decision hyperplane is used in the learning process. The focus of this paper is a new approach that leverages the advantage of both approaches. Specifically, our new model partitions the input space into three parts by creating two additional boundaries in the training process, and then makes the final decision based on a heuristic measurement between the test sample and a subset of selected training samples. Since the original hyperplane used by the underlying original classifier will be eliminated, the proposed model is named the boundary-eliminated (BE) model. Additionally, the pseudoinverse linear discriminant (PILD) is adopted for the BE model so as to obtain a novel classifier abbreviated as BEPILD. Experiments validate both the effectiveness and the efficiency of BEPILD, compared with 13 state-of-the-art classification methods, based on 31 imbalanced and 7 standard data sets.