New Fast Algorithms for Error Rate-Based Stepwise Variable Selection in Discriminant Analysis
Stefan Aeberhard, Olivier Y. de Vel, Danny H. Coomans · SIAM Journal on Scientific Computing · 2000
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.