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

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 me...

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Bibliographic Details
Published in:SIAM journal on scientific computing 2001, Vol.22 (3), p.1036-1052
Main Authors: AEBERHARD, S, DE VEL, O. Y, COOMANS, D. H
Format: Article
Language:English
Subjects:
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Bias
Computer science
control theory
systems
Discriminant analysis
Exact sciences and technology
Feature selection
Investigations
Mathematics
Methods of scientific computing (including symbolic computation, algebraic computation)
Numerical analysis. Scientific computation
Reptiles & amphibians
Sciences and techniques of general use
Theoretical computing
Variables
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Summary: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 $\Lambda$ 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.
ISSN:1064-8275
1095-7197
DOI:10.1137/S1064827596300784