Effective Discretization and Hybrid feature selection using Naïve Bayesian classifier for Medical datamining
Ranjit Abraham, Jay Bharateesh Simha, S. S. Iyengar · International Journal of Computational Intelligence Research · 2009
Abstract: As a probability-based statistical classification method, the Naïve Bayesian classifier has gained wide popularity despite its assumption that attributes are conditionally mutually independent given the class label. Improving the predictive accuracy and achieving dimensionality reduction for statistical classifiers has been an active research area in datamining. 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 Bayesian statistical classifiers. We propose a Hybrid feature selection algorithm (CHI-WSS) that helps in achieving dimensionality reduction by removing irrelevant data, increasing learning accuracy and improving result comprehensibility. Experimental results suggest that on