Artificial neural network — Naïve bayes fusion for solving classification problem of imbalanced dataset

Incorporating knowledge from domain expert to a classifier is one of the techniques which require to be considered in solving imbalanced dataset problems. In this study, the proposed technique is a development to extend the process for imbalanced dataset where the individual classification system has already been designed for balanced data set. This paper introduces a methodology and preliminary results which are used to investigate whether the proposed approach is possible to improve a classifier´s performance when domain expert is employed to the naïve bayes classifier. Domain expert is an additional knowledge which is produced by expert system (neural network) and then become an additional input to the naïve bayes classifier. By using several benchmark data sets from the UCI Machine Learning Repository, the results of the proposed technique show an improvement as compared to the conventional naïve bayes classifier.