Robust feature induction for support vector machines

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Robust feature induction for support vector machines
The goal of feature induction is to automatically create nonlinear combinations of existing features as additional input features to improve classification accuracy. Typically, nonlinear features are introduced into a support vector machine (SVM) through a nonlinear kernel function. One disadvantage of such an approach is that the feature space induced by a kernel function is usually of high dimension and therefore will substantially increase the chance of over-fitting the training data. Another disadvantage is that nonlinear features are induced implicitly and therefore are difficult for people to understand which induced features are critical to the classification performance. In this paper, we propose a boosting-style algorithm that can explicitly induces important nonlinear features for SVMs. We present empirical studies with discussion to show that this approach is effective in improving classification accuracy for SVMs. The comparison with an SVM model using nonlinear kernels al...
Rong Jin, Huan Liu
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2004
Where ICML
Authors Rong Jin, Huan Liu
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