Please use this identifier to cite or link to this item:
|Title:||A sequential feature extraction approach for naïve bayes classification of microarray data||Authors:||Fan, L.
Independent component analysis (ICA)
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 16, 2020
WEB OF SCIENCETM
checked on Jan 9, 2020
checked on Dec 30, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.