Memorias de investigación
Research Publications in journals:
Directional naive Bayes classifiers
Year:2013

Research Areas
  • Artificial intelligence,
  • Statistics

Information
Abstract
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.
International
Si
JCR
Si
Title
Pattern Analysis And Applications
ISBN
1433-7541
Impact factor JCR
0,739
Impact info
Datos JCR del año 2011
Volume
10.1007/s10044-013-0340-z
Journal number
From page
in
To page
press
Month
SIN MES
Ranking
Participants

Research Group, Departaments and Institutes related
  • Creador: Departamento: Inteligencia Artificial