计算机应用研究2018，Vol.35Issue(2)：346-348,353,4.DOI:10.3969/j.issn.1001-3695.2018.02.006
基于MTS-AdaBoost的不平衡数据分类研究
Classification of unbalanced data based on MTS-AdaBoost
摘要
Abstract
Unbalanced data are widely used in practical applications,but most of the traditional classification algorithms assume class distribution balance.Therefore,solving the problem of unbalanced data classification has become one of the bottlenecks in data mining.MTS is a multivariate pattern recognition method,which is combined with the AdaBoost integration algorithm to form the MTS-AdaBoost algorithm.The algorithm used the MTS as the base classifier,and adjusted the probability of the sample in the next base classifier according to the prediction result of the previous base classifier,so as to change the balance degree of the different class data.Finally,this paper applied this method to research the financial crisis warning of listed companies from 2010 to 2015.The result shows that MTS-AdaBoost algorithm's dimensionality reduction and classification results are both superior to traditional MTS,and they are also superior to other commonly used single classifiers.关键词
马田系统/AdaBoost集成算法/不平衡数据/财务危机预警/分类Key words
Mahalanobis-Taguchi system (MTS)/AdaBoost integrated algorithm/unbalanced data/financial crisis warning/classification分类
信息技术与安全科学引用本文复制引用
顾玉萍,程龙生..基于MTS-AdaBoost的不平衡数据分类研究[J].计算机应用研究,2018,35(2):346-348,353,4.基金项目
国家自然科学基金资助项目(71271114) （71271114）
