文献类型：期刊文献

英文题名：Cost-sensitive matrixized classification learning with information entropy

作者：Wang, Zhe[1,2];Chu, Xu[1,2];Li, Dongdong[2];Yang, Hai[2];Qu, Weichao[2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy 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：2022

卷号：116

外文期刊名：APPLIED SOFT COMPUTING

收录：;EI(收录号：20220211449165);WOS:【SCI-EXPANDED(收录号:WOS:000736979400002)】；

基金：This work is supported by Shanghai Science and Technology Program, China "Distributed and generative few-shot algorithm and theory research"under Grant No. 20511100600, Shanghai Science and Technology Program, China "Federated based cross-domain and cross-task incremental learning"under Grant No. 21511100800 and Natural Science Foundation of China under Grant No. 6207 6094.

语种：英文

外文关键词：Cost-sensitive learning; Matrixized learning; Information entropy; Image classification; Pattern recognition

摘要：Classifier design is one of the most significant fields in pattern recognition. Most classifiers are measured by classification accuracy, which assumes that all the misclassification cost are the same. In the real world, different misclassifications usually bring different losses. Based on this fact, costsensitive learning is becoming a hot research area in pattern recognition. However, in cost-sensitive learning, examples costs are often difficult to achieve and usually decided by the authors experience. Hence, combining the cost-sensitive learning and matrixized learning thoughts, we propose a two-class cost-sensitive matrixized classification model based on information entropy called CsMatMHKS in this paper. The proposed CsMatMHKS introduces information entropy which can reveal the uncertainty of one sample into matrixized learning framework to decrease the total misclassification cost. The experimental results on the UCI datasets and image datasets indicate that the CsMatMHKS not only reduces the sum of classification costs but also has comparable classification accuracy. (c) 2021 Elsevier B.V. All rights reserved.