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The Effects of Outliers on Support Vector Machines

Many techniques have been developed for mitigating the effects of outliers on the results of SVM classification, including fuzzy-SVMs and weighted-SVMs. This paper examines the effect that outliers have on weighted-SVMs and standard SVMs on artificially generated data, using linear, radial basis function, and polynomial kernels. We find that SVMs are robust in the presence of noise – outliers caused by mislabeled data.