Combination of Na?ve Bayes Classifier and K-Nearest Neighbor (cNK) in the Classification Based Predictive Models

摘要

In this study, we present a new classifier that combines the distance-based algorithm K-Nearest Neighbor and statistical based Na?ve Bayes Classifier. That is equipped with the power of both but avoid their weakness. The performance of the proposed algorithm in terms of accuracy is experimented on some standard datasets from the machine-learning repository of University of California and compared with some of the art algorithms. The experiments show that in most of the cases the proposed algorithm outperforms the other to some extent. Finally we apply the algorithm for predicting profitability positions of some financial institutions of Bangladesh using data provided by the central bank.