Feature selection toolbox software package

Pavel Pudil

doi:10.1016/S0167-8655(01)00180-5

2002-02-01

PRE

Pavel Pudil

Jana Novovičová

DOI：10.1016/S0167-8655(01)00180-5

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摘要

En 中文

Recent advances in the statistical methodology for selecting optimal subsets of features for data representation and classification are presented. The paper attempts to provide a guideline which approach to choose with respect to the extent of a priori knowledge of the problem. Two basic approaches are reviewed and the conditions under which they should be used are specified. One approach involves the use of the computationally effective floating search methods. The alternative approach trades off the requirement for a priori information for the requirement of sufficient data to represent the distributions involved. Owing to its nature it is particularly suitable for cases when the underlying probability distributions are not unimodal. The approach attempts to achieve simultaneous feature selection and decision rule inference. According to the criterion adopted there are two variants allowing the selection of features either for optimal representation or discrimination. A consulting system aimed to guide a user to choose a proper method for the problem at hand is being prepared. (C) 2002 Elsevier Science B.V. All rights reserved.

Keyword:

pattern recognition

feature selection

search methods

floating search algorithms