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Incremental Discretization for Naïve Bayes Learning with Optimum Binning

Kamal Sutaria, Amit Ganatra, Y.P. Kosta, C.K. Bhensdadia, Kruti Khalpada

Abstract


Incremental Flexible Frequency Discretization (IFFD)
is a recently proposed discretization approach for Naïve Bayes (NB).IFFD performs satisfactory by setting the minimal interval frequency for discretized intervals as a fixed number. In this paper, we first argue that this setting cannot guarantee that the selecting MinBinSize is on always optimal for all the different datasets. So the performance of Naïve Bayes is not good in terms of classification error. We thus proposed a sequential search method for NB: named Optimum Binning. Experiments were conducted on 4 datasets from UCI machine learning repository and performance was compared between NB trained on the data discretized by OB, IFFD, and PKID.


Keywords


Discretization, Naïve Bayes, Optimum Binning

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