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Feature selection often aims to select a compact feature subset to build a pattern classifier with reduced complexity, so as to achieve improved classification performance. From the perspective of pattern analysis, producing stable or robust solution is also a desired property of a feature selection algorithm. However, the issue of robustness is often overlooked in feature selection. In this study,...
Feature ranking, which ranks features via their individual importance, is one of the frequently used feature selection techniques. Traditional feature ranking criteria are apt to produce inconsistent ranking results even with light perturbations in training samples when applied to high dimensional and small-sized gene expression data. A widely used strategy for solving the inconsistencies is the multi-criterion...
Gene selection is one of the major issues in microarray gene expression data analysis. Among existing gene selection techniques, wrapper methods usually produce better selection results than filter methods and embedded methods. The wrapper methods employ a predefined classification algorithm and evaluate the goodness of gene subsets in terms of classification error, which is usually obtained through...
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