Directional naive Bayes classifiers

López-Cruz, Pedro L. and Bielza Lozoya, María Concepción and Larrañaga Múgica, Pedro María (2015). Directional naive Bayes classifiers. "Pattern Analysis And Applications", v. 18 (n. 2); pp. 225-246. ISSN 1433-7541.


Title: Directional naive Bayes classifiers
  • López-Cruz, Pedro L.
  • Bielza Lozoya, María Concepción
  • Larrañaga Múgica, Pedro María
Item Type: Article
Título de Revista/Publicación: Pattern Analysis And Applications
Date: 2015
ISSN: 1433-7541
Volume: 18
Freetext Keywords: Supervised classification; Naive Bayes classifier; Directional statistics; von Mises distribution; von Mises–Fisher distribution
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Inteligencia Artificial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Directional data are ubiquitous in science. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises–Fisher distributions, should be used to deal with this kind of information. We extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and directional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are then evaluated over eight datasets, showing competitive performances against other naive Bayes classifiers that use Gaussian distributions or discretization to manage directional data.

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Item ID: 41014
DC Identifier:
OAI Identifier:
DOI: 10.1007/s10044-013-0340-z
Official URL:
Deposited by: Memoria Investigacion
Deposited on: 25 Oct 2016 07:54
Last Modified: 25 Oct 2016 07:54
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