Simultaneous classification and feature selection via convex quadratic programming with application to HIV-associated neurocognitive disorder assessment

DOI10.1016/j.ejor.2010.03.017zbMath1188.90235OpenAlexW2001768088WikidataQ58796904 ScholiaQ58796904MaRDI QIDQ976339

Michelle Dunbar, John M. Murray, Lucette A. Cysique, Bruce J. Brew, Vaithilingam Jeyakumar

Publication date: 11 June 2010

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2010.03.017

zbMATH Keywords

classificationquadratic optimizationHIVsupport vector machinesfeature selectionneurocognitive disordernonnegativity constraints

Mathematics Subject Classification ID

Convex programming (90C25) Multi-objective and goal programming (90C29) Learning and adaptive systems in artificial intelligence (68T05) Medical applications (general) (92C50)

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Uses Software

• UCI-ml
• ElemStatLearn

Cites Work

• Unnamed Item
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• A feature selection Newton method for support vector machine classification
• Knowledge-based semidefinite linear programming classifiers
• 10.1162/15324430152748218
• Multicategory proximal support vector machine classifiers
• The elements of statistical learning. Data mining, inference, and prediction
• Gene selection for cancer classification using support vector machines