Partition-conditional ICA for Bayesian classification of microarray data

Liwei Fan

doi:10.1016/j.eswa.2010.05.068

Partition-conditional ICA for Bayesian classification of microarray data

2010-12-01

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范丽伟 (Liwei Fan) *

周鹏 (Peng Zhou)

DOI：10.1016/j.eswa.2010.05.068

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Accurate classification of microarray data is very important for medical decision making. Past studies have shown that class-conditional independent component analysis (CC-ICA) is capable of improving the performance of naive 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 naive Bayes classification of microarray data. (C) 2010 Elsevier Ltd. All rights reserved.

Keyword:

Independent component analysis

Microarray data

Naive Bayes

Feature extraction

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Partition-conditional ICA for Bayesian classification of microarray data

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