Fβ Support Vector Machines
نویسنده
چکیده
We introduce in this paper Fβ SVMs, a new parametrization of support vector machines. It allows to optimize a SVM in terms of Fβ , a classical information retrieval criterion, instead of the usual classification rate. Experiments illustrate the advantages of this approach with respect to the traditionnal 2norm soft-margin SVM when precision and recall are of unequal importance. An automatic model selection procedure based on the generalization Fβ score is introduced. It relies on the results of Chapelle, Vapnik et al. [4] about the use of gradient-based techniques in SVM model selection. The derivatives of a Fβ loss function with respect to the hyperparameters C and the width σ of a gaussian kernel are formally defined. The model is then selected by performing a gradient descent of the Fβ loss function over the set of hyperparameters. Experiments on artificial and real-life data show the benefits of this method when the Fβ score is considered.
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