A Selective Naïve Bayesian Classification Algorithm Based on Rough Set

Yin E Hu; Ke Luo

doi:10.4028/www.scientific.net/AMM.325-326.1593

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 325-326A Selective Naïve Bayesian Classification...

A Selective Naïve Bayesian Classification Algorithm Based on Rough Set

Abstract:

Naive Bayesian classifier (NBC) is a simple and effective classification model, but its condition independence assumption is often violated in reality and makes it perform poorly. In our study, we attempt to improve the NBC model through the way of attribute selection based on rough set. The main idea of the improvement model is to select a closest approximate independent attributes subset and relax the assumption of independence. Through the experimental comparison and analysis on the UCI datasets, the model is proved effective.

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Periodical:

Applied Mechanics and Materials (Volumes 325-326)

Pages:

1593-1596

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.325-326.1593

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Online since:

June 2013

Authors:

Yin E Hu, Ke Luo

Keywords:

Attribute Selection, Condition Independence Assumption, Naive Bayesian, Rough Set (RS)

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References

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