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Mathematical Problems in Engineering
Volume 2015, Article ID 170324, 7 pages
Research Article

Bayesian Prediction Model Based on Attribute Weighting and Kernel Density Estimations

1Weifang University of Science & Technology, Shouguang, Shandong 262-700, China
2Department of Computer and Information Engineering, Dongseo University, 47, Churye-Ro, Sasang-Gu, Busan 617-716, Republic of Korea

Received 26 March 2015; Accepted 29 July 2015

Academic Editor: Fons J. Verbeek

Copyright © 2015 Zhong-Liang Xiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Although naïve Bayes learner has been proven to show reasonable performance in machine learning, it often suffers from a few problems with handling real world data. First problem is conditional independence; the second problem is the usage of frequency estimator. Therefore, we have proposed methods to solve these two problems revolving around naïve Bayes algorithms. By using an attribute weighting method, we have been able to handle conditional independence assumption issue, whereas, for the case of the frequency estimators, we have found a way to weaken the negative effects through our proposed smooth kernel method. In this paper, we have proposed a compact Bayes model, in which a smooth kernel augments weights on likelihood estimation. We have also chosen an attribute weighting method which employs mutual information metric to cooperate with the framework. Experiments have been conducted on UCI benchmark datasets and the accuracy of our proposed learner has been compared with that of standard naïve Bayes. The experimental results have demonstrated the effectiveness and efficiency of our proposed learning algorithm.