Pairwise local Fisher and naive Bayes: Improving two standard discriminants

Håkon Otneim, Martin Jullum and Dag Tjøstheim

Journal of Econometrics, 2020, vol. 216, issue 1, 284-304

Abstract: The Fisher discriminant is probably the best known likelihood discriminant for continuous data. Another benchmark discriminant is the naive Bayes, which is based on marginals only. In this paper we extend both discriminants by modeling dependence between pairs of variables. In the continuous case this is done by local Gaussian versions of the Fisher discriminant. In the discrete case the naive Bayes is extended by taking geometric averages of pairwise joint probabilities. We also indicate how the two approaches can be combined for mixed continuous and discrete data. The new discriminants show promising results in a number of simulation experiments and real data illustrations.

Keywords: Kernel estimator; Local Fisher discriminant; Naive Bayes discriminant; Pairwise dependence; Local Gaussian density (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:216:y:2020:i:1:p:284-304

DOI: 10.1016/j.jeconom.2020.01.019

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