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Evaluating Stability of Single and Multiple Feature Selectors that Optimize Feature Subset Cardinality
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SYSNO ASEP 0325643 Document Type V - Research Report R&D Document Type The record was not marked in the RIV Title Evaluating Stability of Single and Multiple Feature Selectors that Optimize Feature Subset Cardinality Title Vyhodnocení stability jednotlivých metod i skupin metod výběru příznaků, který optimalizují kardinalitu podmnožiny příznaků Author(s) Somol, Petr (UTIA-B) RID
Novovičová, Jana (UTIA-B)Issue data Praha: ÚTIA AV ČR, 2009 Series Research Report Series number 2251 Number of pages 38 s. Language eng - English Country CZ - Czech Republic Keywords feature selection ; stability measure ; consistency measure ; feature subset size optimization ; sequential search ; floating search ; individual ranking ; feature selection evaluation Subject RIV BB - Applied Statistics, Operational Research R&D Projects GA102/07/1594 GA ČR - Czech Science Foundation (CSF) GA102/08/0593 GA ČR - Czech Science Foundation (CSF) 1M0572 GA MŠMT - Ministry of Education, Youth and Sports (MEYS) CEZ AV0Z10750506 - UTIA-B (2005-2011) Annotation 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 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 usable for assessing feature selection methods (or criteria) as such Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2010
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