Abstract
An algorithm that identifies the variable subsets with most discriminatory power (in a predictive sense) is proposed. This algorithm minimizes parametric estimates of the error rate among all the possible variable subsets, evaluating only a fraction of the total number of subsets. The computational feasibility is illustrated by simulation experiments.
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© 1998 Springer-Verlag Berlin · Heidelberg
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Duarte Silva, A.P. (1998). A “Leaps and Bounds” Algorithm for Variable Selection in Two-Group Discriminant Analysis. In: Rizzi, A., Vichi, M., Bock, HH. (eds) Advances in Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-72253-0_31
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DOI: https://doi.org/10.1007/978-3-642-72253-0_31
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