NDLI: Experimental analysis of naïve Bayes classifier based on an attribute weighting framework with smooth kernel density estimations

Content Provider	Springer Nature Link
Author	Xiang, Zhong Liang Yu, Xiang Ru Kang, Dae Ki
Copyright Year	2015
Abstract	Naïve Bayes learners are widely used, efficient, and effective supervised learning methods for labeled datasets in noisy environments. It has been shown that naïve Bayes learners produce reasonable performance compared with other machine learning algorithms. However, the conditional independence assumption of naïve Bayes learning imposes restrictions on the handling of real-world data. To relax the independence assumption, we propose a smooth kernel to augment weights for the likelihood estimation. We then select an attribute weighting method that uses the mutual information metric to cooperate with the proposed framework. A series of experiments are conducted on 17 UCI benchmark datasets to compare the accuracy of the proposed learner against that of other methods that employ a relaxed conditional independence assumption. The results demonstrate the effectiveness and efficiency of our proposed learning algorithm. The overall results also indicate the superiority of attribute-weighting methods over those that attempt to determine the structure of the network.
Starting Page	611
Ending Page	620
Page Count	10
File Format	PDF
ISSN	0924669X
Journal	Applied Intelligence
Volume Number	44
Issue Number	3
e-ISSN	15737497
Language	English
Publisher	Springer US
Publisher Date	2015-10-22
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Attribute weighting Mutual information Smooth Kernel density estimation framework Bayesian models Artificial Intelligence (incl. Robotics) Mechanical Engineering Manufacturing, Machines, Tools
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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