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Some theory for Fisher’s linear discriminant function, ‘naive Bayes’, and some alternatives when there are many more variables than observations. (English) Zbl 1064.62073
Summary: We show that the ‘naive Bayes’ classifier which assumes independent covariates greatly outperforms the Fisher linear discriminant rule under broad conditions when the number of variables grows faster than the number of observations, in the classical problem of discriminating between two normal populations. We also introduce a class of rules spanning the range between independence and arbitrary dependence. These rules are shown to achieve Bayes consistency for the Gaussian ‘coloured noise’ model and to adapt to a spectrum of convergence rates, which we conjecture to be minimax.

MSC:
62H30 Classification and discrimination; cluster analysis (statistical aspects)
62F15 Bayesian inference
62M15 Inference from stochastic processes and spectral analysis
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