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|Title:||Partition-conditional ICA for Bayesian classification of microarray data|
Independent component analysis
|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. 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|
|Appears in Collections:||Staff Publications|
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