A comparative analysis of discretization methods for Medical Datamining with Naïve Bayesian classifier

Authors:
Ranjit Abraham;Jay B.Simha;S. S. Iyengar
Affiliations:
TocH Institute of Sci. and Tech., Arakkunnam, Kerala,India;ABIBA Systems, Bangalore, INDIA;Louisiana State University, Baton Rouge
Venue:
ICIT '06 Proceedings of the 9th International Conference on Information Technology
Year:
2006

Citing 0
Cited 3

Data pre-processing: a new algorithm for feature selection and data discretization

CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Review:

The Knowledge Engineering Review
On the interplay of machine learning and background knowledge in image interpretation by Bayesian networks

Artificial Intelligence in Medicine

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Abstract

Naive Bayes classifier has gained wide popularity as a probability-based classification method despite its assumption that attributes are conditionally mutually independent given the class label. This paper makes a study into discretization techniques to improve the classification accuracy of Naïve Bayes with respect to medical datasets. Our experimental results suggest that on an average, with Minimum Description Length (MDL) discretization the Naïve Bayes Classifier seems to be the best performer compared to popular variants of Naïve Bayes as well as some popular non-Naïve Bayes statistical classifiers.