Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015

A new Bayesian classification algorithm based on attribute reduction

Authors
Hongmei Nie, Jiaqing Zhou
Corresponding Author
Hongmei Nie
Available Online December 2015.
DOI
https://doi.org/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.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2015 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering
Part of series
Advances in Computer Science Research
Publication Date
December 2015
ISBN
978-94-6252-133-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmmcce-15.2015.1How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

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  - 2015 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmcce-15.2015.1
DO  - https://doi.org/10.2991/icmmcce-15.2015.1
ID  - Nie2015/12
ER  -