An adaptive feature selection method for microarray data analysis
Bioinformatics and Biomedicine(2012)
Abstract
Feature selection is one of the most important research topics in high dimensional array data analysis. We propose a two-way filtering based method that utilizes a pair of statistics coupled with rigorous cross-validation to identify the most informative features from different types of distributions. We evaluate the utility of the proposed adaptive feature selection method on six MicroArray Quality Control Phase II (MAQC-Π) datasets. The results show that our method yields models with significantly fewer features and can achieve comparable or superior classification performance compared to models generated from other feature selection methods, suggesting high quality feature selection.
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Key words
MicroArray Quality Control Phase,feature selection,adaptive feature selection method,high quality feature selection,method yields model,proposed adaptive feature selection,feature selection method,high dimensional array data,microarray data analysis,different type,informative feature,fewer feature
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