A comparative study of PCA, ICA and class-conditional ICA for Naïve Bayes classifier

Authors:
Liwei Fan;Kim Leng Poh
Affiliations:
Department of Industrial and Systems Engineering, National University of Singapore, Singapore;Department of Industrial and Systems Engineering, National University of Singapore, Singapore
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

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Abstract

The performance of the Naïve Bayes classifier can be improved by appropriate preprocessing procedures. This paper presents a comparative study of three preprocessing procedures, namely Principle Component Analysis (PCA), Independent Component Analysis (ICA) and class-conditional ICA, for Naïve Bayes classifier. It is found that all the three procedures keep improving the performance of the Naïve Bayes classifier with the increase of the number of attributes. Although class-conditional ICA has been found to be superior to PCA and ICA in most cases, it may not be suitable for the case where the sample size for each class is not large enough.