Extended Naive Bayes classifier for mixed data

Naive Bayes induction algorithm is very popular in classification field. Traditional method for dealing with numeric data is to discrete numeric attributes data into symbols. The difference of distinct discredited criteria has significant effect on performance. Moreover, several researches had recently employed the normal distribution to handle numeric data, but using only one value to estimate the population easily leads to the incorrect estimation. Therefore, the research for classification of mixed data using Naive Bayes classifiers is not very successful. In this paper, we propose a classification method, Extended Naive Bayes (ENB), which is capable for handling mixed data. The experimental results have demonstrated the efficiency of our algorithm in comparison with other classification algorithms ex. CART, DT and MLP’s.