A Bayesian Approach to
نویسندگان
چکیده
The performance of many machine learning algorithms can be substantially improved with a proper discretization scheme. In this paper we describe a theoretically rigorous approach to discretization of continuous attribute values, based on a Bayesian clustering framework. The method produces a probabilistic scoring metric for diierent discretizations, and it can be combined with various types of learning algorithms working on discrete data. The approach is validated by demonstrating empirically the performance improvement of the Naive Bayes classiier when Bayesian discretization is used instead of the standard equal frequency interval discretization.
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