کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
478668 | 1446118 | 2010 | 9 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Simultaneous classification and feature selection via convex quadratic programming with application to HIV-associated neurocognitive disorder assessment Simultaneous classification and feature selection via convex quadratic programming with application to HIV-associated neurocognitive disorder assessment](/preview/png/478668.png)
Support vector machines (SVMs), that utilize a mixture of the L1L1-norm and the L2L2-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 HIV-infected individuals.
Journal: European Journal of Operational Research - Volume 206, Issue 2, 16 October 2010, Pages 470–478