Abstract
An important problem in discriminant analysis that has received little attention in the literature is the effect of outliers when variable selection forms part of the analysis. In this paper we argue that variable selection and outlier identification should not be done sequentially, but should rather be integrated. We investigate an integrated approach, and compare its classification performance to that of a sequential approach in a limited simulation study.
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© 2003 Springer-Verlag Berlin Heidelberg
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Steel, S., Louw, N. (2003). Improving the Classification Performance of a Discriminant Rule by Dealing With Data Cases Having a Substantial Influence on Variable Selection. In: Schader, M., Gaul, W., Vichi, M. (eds) Between Data Science and Applied Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18991-3_13
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DOI: https://doi.org/10.1007/978-3-642-18991-3_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40354-8
Online ISBN: 978-3-642-18991-3
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