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https://scholarbank.nus.edu.sg/handle/10635/87162
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 | Citation: | 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. | 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/10635/87162 | ISSN: | 09574174 |
Appears in Collections: | Staff Publications |
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