document: Stable feature selection via dense feature groups Many feature selection algorithms have been proposed in the past focusing on improving classification accuracy . In this work , we point out the importance of stable feature selection for knowledge discovery from high-dimensional data , and identify two causes of instability of feature selection algorithms : selection of a minimum subset without redundant features and small sample size . We propose a general framework for stable feature selection which emphasizes both good generalization and stability of feature selection results . The framework identifies dense feature groups based on kernel density estimation and treats features in each dense group as a coherent entity for feature selection . An efficient algorithm DRAGS ( Dense Relevant Attribute Group Selector ) is developed under this framework . We also introduce a general measure for assessing the stability of feature selection algorithms . Our empirical study based on microarray data verifies that dense feature groups remain stable under random sample hold out , and the DRAGS algorithm is effective in identifying a set of feature groups which exhibit both high classification accuracy and stability .
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