Multi-Objective Learning of Multi-Dimensional Bayesian Classifiers

Authors:
Juan D. Rodríguez;Jose A. Lozano
Affiliations:
-;-
Venue:
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Year:
2008

Citing 0
Cited 6

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Abstract

Multi-dimensional classification is a generalization of supervised classification that considers more than one class variable to classify. In this paper we review the existing multi-dimensional Bayesian classifiers and introduce a new one: the KDB multi-dimensional classifier. Then we define different classification rules for multi-dimensional scope. Finally, we introduce a structural learning approach of a multi-dimensional Bayesian classifier based on the multi-objective evolutionary algorithm NSGA-II. The solution of the learning approach is a Pareto front representing different multi-dimensional classifiers and their accuracy values for the different classes, so a decision maker can easily choose the classifier which is more interesting for the particular problem and domain.