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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Margin Based Sample Weighting for Stable Feature Selection
verfasst von : Yue Han, Lei Yu
Erschienen in: Web-Age Information Management
Verlag: Springer Berlin Heidelberg
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Stability of feature selection is an important issue in knowledge discovery from high-dimensional data. A key factor affecting the stability of a feature selection algorithm is the sample size of training set. To alleviate the problem of small sample size in high-dimensional data, we propose a novel framework of margin based sample weighting which extensively explores the available samples. Specifically, it exploits the discrepancy among local profiles of feature importance at various samples and weights a sample according to the outlying degree of its local profile of feature importance. We also develop an efficient algorithm under the framework. Experiments on a set of public microarray datasets demonstrate that the proposed algorithm is effective at improving the stability of state-of-the-art feature selection algorithms, while maintaining comparable classification accuracy on selected features.