Regularized classification for mixed continuous and categorical variables under across-location heteroscedasticity

Authors:
Chi-Ying Leung
Affiliations:
Department of Statistics, The Chinese University of Hong Kong, Shatin NT, Hong Kong, China
Venue:
Journal of Multivariate Analysis
Year:
2005

Citing 4
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Abstract

A regularized classifier is proposed for a two-population classification problem of mixed continuous and categorical variables in a general location model(GLOM). The limiting overall expected error for the classifier is given. It can be used in an optimization search for the regularization parameters. For a heteroscedastic spherical dispersion across all locations, an asymptotic error is available which provides an alternative criterion for the optimization search. In addition, the asymptotic error can serve as a baseline for practical comparisons with other classifiers. Results based on a simulation and two real datasets are presented.