A Hybrid Artificial Neural Network-Naive Bayes for solving imbalanced dataset problems in semiconductor manufacturing test process

This paper introduces a hybrid approach, namely Hybrid Artificial Neural Network-Naive Bayes classifier, for two-class imbalanced datasets classification. An imbalanced dataset in semiconductor manufacturing test process is chosen as a case study. Outputs prediction in semiconductor manufacturing is helpful for engineer to identify good/bad products earlier and to avoid the bad units from being processed. This application shows the significance of solving the problems. The proposed hybrid approach presented in this paper uses the concept that an Artificial Neural Network (ANN) provides a guidance to Naive Bayes classifier in making better decision by providing an additional input to Naive Bayes. Several experiments are conducted as comparison to the individual classifiers, which are ANN and Naive Bayes. As a result, the proposed Hybrid approach performs better than the individual classifiers and finally overcomes the imbalanced dataset problems in semiconductor manufacturing test process.