文献类型：期刊文献

英文题名：Entropy and gravitation based dynamic radius nearest neighbor classification for imbalanced problem

作者：Wang, Zhe[1,2];Li, Yanqiong[2];Li, Dongdong[2];Zhu, Zonghai[2];Du, Wenli[1]

机构：[1]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China

年份：2020

卷号：193

外文期刊名：KNOWLEDGE-BASED SYSTEMS

收录：;EI(收录号：20200208026879);WOS:【SCI-EXPANDED(收录号:WOS:000523558800043)】；

基金：This work is supported by Natural Science Foundation of China under Grant No. 61672227, "Shuguang Program'' supported by Shanghai Education Development Foundation, PR China and Shanghai Municipal Education Commission, PR China, Natural Science Foundations of China under Grant No. 61806078, National Science Foundation of China for Distinguished Young Scholars under Grant 61725301, National Major Scientific and Technological Special Project, PR China for "Significant New Drugs Development'' under Grant No. 2019ZX09201004, the Special Fund Project for Shanghai Informatization Development in Big Data, PR China under Grant 201901043, and National Key R&D Program of China under Grant No. 2018YFC0910500.

语种：英文

外文关键词：Information entropy; Gravitational force; Nearest neighbor rules; Imbalanced problem; L-p-norm

摘要：In imbalanced problems, the asymmetric number of samples in different classes brings great challenges to traditional classifiers, especially to the Nearest Neighbors (NN) classifiers. When NN-based classifier deals with imbalanced problems, the criterion of itself makes the classification result data-dependent, thus biasing towards the majority class. To overcome the drawback in NN-based classifiers, a meta heuristic NN-based algorithm named Gravitational Fixed Radius Nearest Neighbor classifier (GFRNN) is proposed to solve imbalanced problems by drawing on Newton's law of universal gravitation. However, GFRNN still has three major problems including negligence of the distribution of samples, unreasonable calculation of data mass and improper distance metric. To this end, this paper proposes an Entropy and Gravitation based Dynamic Radius Nearest Neighbor algorithm (EGDRNN). Different from GFRNN, EGDRNN determines the radius in a dynamic and rapid way. EGDRNN uses entropy information to make samples at different locations have different importance. Finally, by utilizing a general L-p-norm to calculate the distance between two samples, the classification performance is greatly improved. The experimental result validates that the proposed EGDRNN not only achieves the highest classification accuracy but also takes the lowest time consuming among all comparison algorithms. (c) 2020 Elsevier B.V. All rights reserved.