Evolving Extended Naive Bayes Classifiers

Authors:
Frank Klawonn;Plamen Angelov
Affiliations:
University of Applied Sciences BS/WF, Germany;Lancaster University, UK
Venue:
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Year:
2006

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Abstract

Naive Bayes classifiers are a very simple, but often effective tool for classification problems, although they are based on independence assumptions that do not hold in most cases. Extended na篓ýve Bayes classifiers also rely on independence assumptions, but break them down to artificial subclasses, in this way becoming more powerful than ordinary na篓ýve Bayes classifiers. Since the involved computations for Bayes classifiers are basically generalised mean value calculations, they easily render themselves to incremental and online learning. However, for the extended na篓ýve Bayes classifiers it is necessary, to choose and construct the subclasses, a problem whose answer is not obvious, especially in the case of online learning. In this paper we propose an evolving extended na篓ýve Bayes classifier that can learn and evolve in an online manner.