Interval Estimation Naı̈ve Bayes
نویسندگان
چکیده
Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with assumptions of conditional independence among features given the class, called naı̈ve Bayes, is competitive with state of the art classifiers. On this paper a new naive Bayes classifier called Interval Estimation naı̈ve Bayes is proposed. Interval Estimation naı̈ve Bayes performs on two phases. On the first phase an interval estimation of each probability necessary to specify the naı̈ve 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 naı̈ve Bayes classifier. Our new approach has shown to be quite competitive related to simple naı̈ve Bayes. Experimental tests have been done with 21 data sets from the UCI repository.
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