Bayesian feature selection for classification with possibly large number of classes

    Authors

    J. Davis; M. Pensky;W. Crampton

    Comments

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    Abbreviated Journal Title

    J. Stat. Plan. Infer.

    Keywords

    Classification; High-dimensional data; Bayesian feature selection; ANOVA; SHRINKAGE; SIGNAL; Statistics & Probability

    Abstract

    In what follows, we introduce two Bayesian models for feature selection in high-dimensional data, specifically designed for the purpose of classification. We use two approaches to the problem: one which discards the components which have "almost constant" values (Model 1) and another which retains the components for which variations in-between the groups are larger than those within the groups (Model 2). We assume that p > > n, i.e. the number of components p is much larger than the number of samples n, and that only few of those p components are useful for subsequent classification. We show that particular cases of the above two models recover familiar variance or ANOVA-based component selection. When one has only two classes and features are a priori independent, Model 2 reduces to the Feature Annealed Independence Rule (FAIR) introduced by Fan and Fan (2008) and can be viewed as a natural generalization of FAIR to the case of L > 2 classes. The performance of the methodology is studies via simulations and using a biological dataset of animal communication signals comprising 43 groups of electric signals recorded from tropical South American electric knife fishes. (C) 2011 Elsevier B.V. All rights reserved.

    Journal Title

    Journal of Statistical Planning and Inference

    Volume

    141

    Issue/Number

    9

    Publication Date

    1-1-2011

    Document Type

    Article

    Language

    English

    First Page

    3256

    Last Page

    3266

    WOS Identifier

    WOS:000291523100024

    ISSN

    0378-3758

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