Methods to determine the branching attribute in bayesian multinets classifiers

Authors:
A. Cano;J. G. Castellano;A. R Masegosa;S. Moral
Affiliations:
Departamento de Ciencias de la Computación e Inteligencia Artificial, Granada University, Granada, Spain;Departamento de Ciencias de la Computación e Inteligencia Artificial, Granada University, Granada, Spain;Departamento de Ciencias de la Computación e Inteligencia Artificial, Granada University, Granada, Spain;Departamento de Ciencias de la Computación e Inteligencia Artificial, Granada University, Granada, Spain
Venue:
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Year:
2005

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Abstract

Bayesian multinets are a Bayesian networks extension where context-specific conditional independences can be represented. The main aim of this work is to study different methods to choose the distinguished attribute in Bayesian multinets when we use them in supervised classification tasks. We have used different approaches: a wrapper method and several filter methods. This will allow us to determine the most appropriate approach that meets our requirements of accuracy and/or time.