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

Elma Zannatul Ferdousy, Md. Mafijul Islam, M. Abdul Matin

Abstract


In this study, we present a new classifier that combines the distance-based algorithm K-Nearest Neighbor and statistical based Naïve 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.


Full Text: PDF DOI: 10.5539/cis.v6n3p48

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

Computer and Information Science   ISSN 1913-8989 (Print)   ISSN 1913-8997 (Online)
Copyright © Canadian Center of Science and Education

To make sure that you can receive messages from us, please add the 'ccsenet.org' domain to your e-mail 'safe list'. If you do not receive e-mail in your 'inbox', check your 'bulk mail' or 'junk mail' folders.