New fast algorithms for error rate-based stepwise variable selection in discriminant analysis

Aeberhard, S., De Vel, O.Y., and Coomans, D.H. (2000) New fast algorithms for error rate-based stepwise variable selection in discriminant analysis. Siam Journal on Scientific Computing, 22 (3). pp. 1036-1052.

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Abstract

Variable selection is an important technique for reducing the dimensionality in multivariate predictive discriminant analysis and classification. In the past, direct evaluation of the subsets by means of a classifier has been computationally too expensive, rendering necessary the use of heuristic measures of class separation, such as Wilk's Λ or the Mahalanobis distance between class means. We present new fast algorithms for stepwise variable selection based on quadratic and linear classifiers with time complexities which, to within a constant, are the same as those applying measures of class separation. Comparing the new algorithms to previous implementations of classifier-based variable selection, we show that dramatic speed-ups are achieved.

Item ID: 13023
Item Type: Article (Refereed Research - C1)
Keywords: classification; classifier-based variable selection; dimensionality reduction; error rate
ISSN: 1064-8275
Date Deposited: 16 Jul 2013 05:03
FoR Codes: 01 MATHEMATICAL SCIENCES > 0104 Statistics > 010401 Applied Statistics @ 51%
08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining @ 49%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970101 Expanding Knowledge in the Mathematical Sciences @ 100%
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