Interval Estimation Na¨ ive Bayes
Vı́ctor Robles, José M. Peña, Ernestina Menasalvas, Marı́a S. Pérez · 2003
Abstract. Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with assumptions of conditional independence among features given the class, called naïve Bayes, is competitive with state of the art classifiers. On this paper a new naive Bayes classifier called Interval Estimation naïve Bayes is proposed. Interval Estimation naïve Bayes performs on two phases. On the first phase an interval estimation of each probability necessary to specify the naïve Bayes is estimated. On the second phase the best combination of values inside these intervals is calculated with a heuristic search that is guided by the accuracy of the classifiers. The founded values in the search are the new parameters for the naïve Bayes classifier. Our new approach has shown to be quite competitive related to simple naïve Bayes. Experimental tests have been done with 21 data sets from the UCI repository. 1