Ranjit Abraham

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Naïve 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(More)
Much research work in datamining has gone into improving the predictive accuracy of statistical classifiers by applying the techniques of discretization and feature selection. As a probability-based statistical classification method, the Naïve Bayesian classifier has gained wide popularity despite its assumption that attributes are conditionally mutually(More)
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