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Abstract Details

Activity Number: 338
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #306434
Title: Sparse Forward Selection for Support Vector Classification
Author(s): Subhashis Ghoshal*+ and Hao Helen Zhang and Wookyeon Hwang
Companies: North Carolina State University and North Carolina State University and LG Electronics
Address: Dept. of Statistics-Campus Box 8203, Raleigh, NC, 27695-8203, United States
Keywords: Classification ; forward selection ; high dimension ; support vector machines

We propose a new binary classi fication technique for high dimensional predictors. Usually, only a small fraction of predictors have signifi cant impacts on prediction. By adding an L1-type penalty in the loss function, common classif ication methods such as support vector machines (SVM) can perform variable selection also. We propose a method which can reduce high dimensional optimization problem to one dimensional optimization by iterating the selection step. The proposed method is based on a forward selection version of penalized SVM or its variants. The advantage of optimizing in one dimension is that the location of the optimum solution can be obtained by intelligent search by exploiting convexity and piecewise polynomial structure of the criterion function. In each step, the predictor minimizng the penalized one-dimensional loss function is chosen in the model and the step is iterated until convergence. Simulations show that the new classifi cation rule often has better predictive and selection accuracy.

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