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Pairwise local Fisher and naive Bayes: Improving two standard discriminants

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  • Otneim, Håkon
  • Jullum, Martin
  • Tjøstheim, Dag

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.

Suggested Citation

  • Otneim, Håkon & Jullum, Martin & Tjøstheim, Dag, 2020. "Pairwise local Fisher and naive Bayes: Improving two standard discriminants," Journal of Econometrics, Elsevier, vol. 216(1), pages 284-304.
    • Handle: RePEc:eee:econom:v:216:y:2020:i:1:p:284-304
    DOI: 10.1016/j.jeconom.2020.01.019
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    References listed on IDEAS

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    Cited by:

    1. José-Luis Velázquez-Rodríguez & Yenny Villuendas-Rey & Oscar Camacho-Nieto & Cornelio Yáñez-Márquez, 2020. "A Novel and Simple Mathematical Transform Improves the Perfomance of Lernmatrix in Pattern Classification," Mathematics, MDPI, vol. 8(5), pages 1-46, May.

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