Naive Bayes classifier for continuous variables using novel method (NBC4D) and distributions

In data mining, when using Naive Bayes classification technique, it is necessary to overcome the problem of how to deal with continuous attributes. Most previous work has solved the problem either by using discretization, normal method or kernel method. This study proposes the usage of different continuous probability distribution techniques for Naive Bayes classification. It explores various probability density functions of distributions. The experimental results show that the proposed probability distributions also classify continuous data with potentially high accuracy. In addition, this paper introduces a novel method, named NBC4D, which offers a new approach for classification by applying different distribution types on different attributes. The results (obtained classification accuracy rates) show that our proposed method (the usage of more than one distribution types) has success on real-world datasets when compared with the usage of only one well known distribution type.