A new Bayesian classification algorithm based on attribute reduction

DOI

10.2991/icmmcce-15.2015.1How to use a DOI?

Keywords

Naive Bayesian classifier, attribute reduction,PCA, Attribute correlation coefficient

Abstract

Naive Bayesian classifier is a simple and efficient classification method. However, the assumption of the independence of its attributes is difficult to be satisfied, which influences the classification performance. In this paper, a new classification algorithm is proposed, which is based on the attribute correlation coefficient and principal component analysis. By the algorithm, we can remove the attributes that are not related to the class, and make sure that the retained attributes are independent of each other. By removing redundant attributes, the obtained attribute subset meets the assumption of Naive Bayesian classifier, and ultimately improves the classification performance of Naive Bayesian classifier.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Hongmei Nie
AU  - Jiaqing Zhou
PY  - 2015/12
DA  - 2015/12
TI  - A new Bayesian classification algorithm based on attribute reduction
BT  - Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015
PB  - Atlantis Press
SP  - 1
EP  - 5
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmcce-15.2015.1
DO  - 10.2991/icmmcce-15.2015.1
ID  - Nie2015/12
ER  -