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A Variance Reduction Framework for Stable Feature Selection
2010
2010 IEEE International Conference on Data Mining
Besides high accuracy, stability of feature selection has recently attracted strong interest in knowledge discovery from high-dimensional data. In this study, we present a theoretical framework about the relationship between the stability and accuracy of feature selection based on a formal bias-variance decomposition of feature selection error. The framework also suggests a variance reduction approach for improving the stability of feature selection algorithms. Furthermore, we propose an
doi:10.1109/icdm.2010.144
dblp:conf/icdm/HanY10
fatcat:3zipero2f5dlxgjjpjvlzp5ojm