Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.eswa.2010.05.068
Title: Partition-conditional ICA for Bayesian classification of microarray data
Authors: Fan, L.
Poh, K.-L. 
Zhou, P.
Keywords: Feature extraction
Independent component analysis
Microarray data
Mutual information
Naïve Bayes
Issue Date: Dec-2010
Source: Fan, L., Poh, K.-L., Zhou, P. (2010-12). Partition-conditional ICA for Bayesian classification of microarray data. Expert Systems with Applications 37 (12) : 8188-8192. ScholarBank@NUS Repository. https://doi.org/10.1016/j.eswa.2010.05.068
Abstract: Accurate classification of microarray data is very important for medical decision making. Past studies ave shown that class-conditional independent component analysis (CC-ICA) is capable of improving the performance of naïve Bayes classifier in microarray data analysis. However, when a microarray dataset has a small number of samples for some classes, the application of CC-ICA may become infeasible. This paper extends CC-ICA and proposes a partition-conditional independent component analysis (PC-ICA) method for naive Bayes classification of microarray data. Compared to ICA and CC-ICA, PC-ICA represents an in-between concept for feature extraction. Our experimental results on two microarray datasets show that PC-ICA is more effective than ICA in improving the performance of naïve Bayes classification of microarray data. © 2010 Elsevier Ltd. All rights reserved.
Source Title: Expert Systems with Applications
URI: http://scholarbank.nus.edu.sg/handle/10635/63252
ISSN: 09574174
DOI: 10.1016/j.eswa.2010.05.068
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