Classification Algorithm for Naïve Bayes Based on Validity and Correlation

Abstract:

Naïve Bayes classification algorithm based on validity (NBCABV) optimizes the training data by eliminating the noise samples of training data with validity to improve the effect of classification, while it ignores the associations of properties. In consideration of the associations of properties, an improved method that is classification algorithm for Naïve Bayes based on validity and correlation (CANBBVC) is proposed to delete more noise samples with validity and correlation, thus resulting in better classification performance. Experimental results show this model has higher classification accuracy comparing the one based on validity solely.

• Periodical: Applied Mechanics and Materials (Volumes 303-306)
• Pages: 1609-1612
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.303-306.1609
• Online since: February 2013
• Authors: Huai Lin Dong, Xiao Dan Zhu, Qing Feng Wu, Juan Juan Huang
• Keywords: Correlation, Naive Bayes, Validity

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