计算机应用研究2018，Vol.35Issue(2)：342-345,4.DOI:10.3969/j.issn.1001-3695.2018.02.005
基于证据理论的不平衡数据半监督分类方法
Semi-supervised classification method for imbalanced data based on evidence theory
摘要
Abstract
This paper proposed a semi-supervised classification method based on evidence theory and biased-SVM for imbalance data sets which had a number of unlabeled samples.First,the method used the stochastic subspace method to get different views.Second,it trained biased-SVM model using the initial labelled samples on each view,then the trained model was applied to unlabled samples to get probability outputs.At last,it adopted evidence theory to improve the stability of unlabeled samples signatures.Experimental results on some public data sets show that compared with other methods,the proposed approach can more effectively and stably utilize the unlabeled examples to improve the value of G-mean and minority class F-value under the different rate of labelled sample.关键词
半监督分类/不平衡数据/证据理论/biased-SVMKey words
semi-supervised classification/imbalanced data/evidence theory/biased-SVM分类
信息技术与安全科学引用本文复制引用
杜利敏,徐扬..基于证据理论的不平衡数据半监督分类方法[J].计算机应用研究,2018,35(2):342-345,4.基金项目
国家自然科学基金资助项目(61673320) （61673320）
国家自然科学基金青年科学基金资助项目(61305074) （61305074）
