Feature selection with complexity measure in a quadratic programming setting
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Feature selection stability assessment based on the Jensen-Shannon divergence
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Sparse and stable gene selection with consensus SVM-RFE
Pattern Recognition Letters
Feature extraction in protein sequences classification: a new stability measure
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Toward an efficient and scalable feature selection approach for internet traffic classification
Computer Networks: The International Journal of Computer and Telecommunications Networking
Simultaneous sample and gene selection using t-score and approximate support vectors
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
Analysis of feature selection stability on high dimension and small sample data
Computational Statistics & Data Analysis
A survey on feature selection methods
Computers and Electrical Engineering
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Stability (robustness) of feature selection methods is a topic of recent interest, yet often neglected importance, with direct impact on the reliability of machine learning systems. We investigate the problem of evaluating the stability of feature selection processes yielding subsets of varying size. We introduce several novel feature selection stability measures and adjust some existing measures in a unifying framework that offers broad insight into the stability problem. We study in detail the properties of considered measures and demonstrate on various examples what information about the feature selection process can be gained. We also introduce an alternative approach to feature selection evaluation in the form of measures that enable comparing the similarity of two feature selection processes. These measures enable comparing, e.g., the output of two feature selection methods or two runs of one method with different parameters. The information obtained using the considered stability and similarity measures is shown to be usable for assessing feature selection methods (or criteria) as such.