Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.eswa.2009.01.075
Title: A sequential feature extraction approach for naïve bayes classification of microarray data
Authors: Fan, L.
Poh, K.-L. 
Zhou, P.
Keywords: Feature extraction
Independent component analysis (ICA)
Microarray data
Naïve Bayes
Stepwise regression
Issue Date: Aug-2009
Citation: Fan, L., Poh, K.-L., Zhou, P. (2009-08). A sequential feature extraction approach for naïve bayes classification of microarray data. Expert Systems with Applications 36 (6) : 9919-9923. ScholarBank@NUS Repository. https://doi.org/10.1016/j.eswa.2009.01.075
Abstract: Accurate classification of microarray data plays a vital role in cancer prediction and diagnosis. Previous studies have demonstrated the usefulness of naïve Bayes classifier in solving various classification problems. In microarray data analysis, however, the conditional independence assumption embedded in the classifier itself and the characteristics of microarray data, e.g. the extremely high dimensionality, may severely affect the classification performance of naïve Bayes classifier. This paper presents a sequential feature extraction approach for naïve Bayes classification of microarray data. The proposed approach consists of feature selection by stepwise regression and feature transformation by class-conditional independent component analysis. Experimental results on five microarray datasets demonstrate the effectiveness of the proposed approach in improving the performance of naïve Bayes classifier in microarray data analysis. © 2009 Elsevier Ltd. All rights reserved.
Source Title: Expert Systems with Applications
URI: http://scholarbank.nus.edu.sg/handle/10635/62951
ISSN: 09574174
DOI: 10.1016/j.eswa.2009.01.075
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