Feature selection via sensitivity analysis of SVM probabilistic outputs

DOI10.1007/S10994-007-5025-7zbMath1470.68175OpenAlexW2151208049WikidataQ56986426 ScholiaQ56986426MaRDI QIDQ1009227

J. Martínez

Publication date: 31 March 2009

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-007-5025-7

zbMATH Keywords

sensitivitysupport vector machinesfeature selectionfeature ranking

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (4)

Sensitivity analysis in classification using Bayesian smoothing spline ANOVA probit regression ⋮ A DC programming approach for feature selection in support vector machines learning ⋮ A survey on semi-supervised feature selection methods ⋮ Feature importance ranking for classification in mixed online environments

Uses Software

• UCI-ml
• SVMlight
• LIBSVM

Cites Work

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• Bagging predictors
• Feature extraction. Foundations and applications. Papers from NIPS 2003 workshop on feature extraction, Whistler, BC, Canada, December 11--13, 2003. With CD-ROM.
• Wrappers for feature subset selection
• Classification by pairwise coupling
• Support-vector networks
• Bayesian Trigonometric Support Vector Classifier
• 10.1162/153244303322753616
• 10.1162/153244303322753706
• Combined SVM-based feature selection and classification
• Soft margins for AdaBoost
• Random forests
• Choosing multiple parameters for support vector machines
• Gene selection for cancer classification using support vector machines

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