Incremental discretization for Naïve-Bayes classifier

Authors:
Jingli Lu;Ying Yang;Geoffrey I. Webb
Affiliations:
Clayton School of Information Technology, Monash University, VIC, Australia;Clayton School of Information Technology, Monash University, VIC, Australia;Clayton School of Information Technology, Monash University, VIC, Australia
Venue:
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Year:
2006

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Abstract

Naïve-Bayes classifiers (NB) support incremental learning. However, the lack of effective incremental discretization methods has been hindering NB’s incremental learning in face of quantitative data. This problem is further compounded by the fact that quantitative data are everywhere, from temperature readings to share prices. In this paper, we present a novel incremental discretization method for NB, incremental flexible frequency discretization (IFFD). IFFD discretizes values of a quantitative attribute into a sequence of intervals of flexible sizes. It allows online insertion and splitting operation on intervals. Theoretical analysis and experimental test are conducted to compare IFFD with alternative methods. Empirical evidence suggests that IFFD is efficient and effective. NB coupled with IFFD achieves a rapport between high learning efficiency and high classification accuracy in the context of incremental learning.