Simple tiered classifiers

• 成果类型: Article
• 署名作者: Hall, Peter; Xia, Yingcun; Xue, Jing-Hao
• 署名单位: University of Melbourne; National University of Singapore; University of London; University College London
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/ass086
• 发表日期: 2013
• 页码: 431445
• 关键词: mixtures

摘要

In this paper we propose simple, general tiered classifiers for relatively complex data. Empirical studies on real and simulated data show that three two-tier classifiers, which are respective extensions of linear discriminant analysis, linear logistic regression and support vector machines, can reduce noticeably the relatively high misclassification error of their original single-tier counterparts, without significantly increasing computational labour.