Evolutionary Learning of Dynamic Naive Bayesian Classifiers

Miguel A. Palacios-Alonso, Carlos A. Brizuela, L. Enrique Sucar

Naive Bayesian classifiers work well in data sets with independent attributes. However, they perform poorly when the attributes are dependent or when there are one or more irrelevant attributes which are dependent of some relevant ones. Therefore, to increase this classifier accuracy we need a method to design network structures that can capture the dependencies and get rid of irrelevant attributes. Furthermore, when we deal with dynamical processes there are temporal relations that should be considered in the network design. We propose an evolutionary optimization algorithm to solve this design problem. We introduce a new encoding scheme and new genetic operators which are natural extensions of previously proposed encoding and operators for grouping problems. The design methodology is applied to solve the recognition problem for nine hand gestures. Experimental results show that the evolved network has higher average classification accuracy than the basic dynamic naive Bayesian classifier.

Subjects: 1. Applications; 1.9 Genetic Algorithms

Submitted: Feb 25, 2008


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