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

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Page: 470-478

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Dunbar, Michelle E; Murray, John M; Cysique, Lucette A; Brew, Bruce J; Jeyakumar, Vaithilingam
assessment; simultaneous; convex; disorder; via; selection; neurocognitive; feature; associated; classification; hiv; application; programming; quadratic; Engineering; Science and Technology Studies
article description
Support vector machines (SVMs), that utilize a mixture of the L1-norm and the L2-norm penalties, are capable of performing simultaneous classification and selection of highly correlated features. These SVMs, typically set up as convex programming problems, are re-formulated here as simple convex quadratic minimization problems over non-negativity constraints, giving rise to a new formulation - the pq SVM method. Solutions to our re-formulation are obtained efficiently by an extremely simple algorithm. Computational results on a range of publicly available datasets indicate that these methods allow greater classification accuracy in addition to selecting groups of highly correlated features. These methods were also compared on a new dataset assessing HIV-associated neurocognitive disorder in a group of 97 HIVinfected individuals.