Facilitating high-dimensional transparent classification via empirical Bayes variable selection

Bar Haim; Booth James; Wells Martin T.Liu Kangyan

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Facilitating high-dimensional transparent classification via empirical Bayes variable selection

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摘要

We present a two-step approach to classification problems in the large P, small N setting, where the number of predictors may be larger than the sample size. We assume that the association between the predictors and the class variable has an approximate linear-logistic form, but we allow the class boundaries to be nonlinear. We further assume that the number of true predictors is relatively small. In the first step, we use a binomial generalized linear model to identify which predictors are associated with each class and then restrict the data set to these predictors and run a nonlinear classifier, such as a random forest or a support vector machine. We show that, without the variable screening step, the classification performance of both the random forest and support vector machine is degraded when many among the P predictors are not related to the class.

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来源
《Applied stochastic models in business and industry》 |2018年第6期|949-961|共13页
作者
Bar Haim; Booth James; Wells Martin T.Liu Kangyan;
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作者单位

Cornell Univ, Dept Biol Stat & Computat Biol, Ithaca, NY USA;

Univ Connecticut, Dept Stat, Storrs, CT 06269 USA;

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原文格式 PDF
正文语种英语
中图分类应用数学;
关键词
EM algorithm; generalized linear models; random forest; support vector machines; variable selection;

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Facilitating high-dimensional transparent classification via empirical Bayes variable selection

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