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

Authors

Michelle E. Dunbar, University of New South WalesFollow
John M. Murray, University Of New South Wales
Lucette A. Cysique, University Of New South Wales
Bruce J. Brew, University Of New South Wales
Vaithilingam Jeyakumar, University Of New South Wales

RIS ID

82832

Publication Details

Dunbar, M. E., Murray, J. M., Cysique, L. A., Brew, B. J. & Jeyakumar, V. (2010). Simultaneous classification and feature selection via convex quadratic programming with application to HIV-associated neurocognitive disorder assessment. European Journal of Operational Research, 206 (2), 470-478.

Abstract

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.