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