An Improved Weighted Mixed Bayes Classification Model

Wei Xiang Xu; Ling Yan Sun; Xu Min Liu

doi:10.4028/www.scientific.net/AMR.179-180.1260

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 179-180An Improved Weighted Mixed Bayes Classification...

An Improved Weighted Mixed Bayes Classification Model

Abstract:

The paper investigates kinds of classical Bayes classification arithmetic and summarizes their advantage and disadvantage, then introduces an improved weighted mixed Bayes classification model. It divides attribute sets into several subsets theorem. The subsets are trained by TAN (Tree Augmented Naive Bayes) and the results are integrated by weighted formula. At the same, the paper introduced a new method to compute the weights of attribute subsets. Finally, Experimental results show that this model has higher classification accuracy and practicability．

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Periodical:

Advanced Materials Research (Volumes 179-180)

Pages:

1260-1265

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.179-180.1260

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Online since:

January 2011

Authors:

Wei Xiang Xu, Ling Yan Sun, Xu Min Liu

Keywords:

Classification, Data Mining (DM), TaN, Weighted Naïve Bayes

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