STOCS: An efficient self-tuning multiclass classification approach

Abstract

A simple, efficient, and parameter-free approach is proposed for the problem of multiclass classification, and is especially useful when dealing with large-scale datasets in the presence of label noise. Grown out of one-class SVM, our approach enjoys several distinct features: First, its decision boundary is learned based on both positive and negative examples; Second, the internal parameters and especially the kernel bandwidth are self-tuned. Our approach is compared side-by-side with LIBSVM, arguably the most widely-used multiclass classification system, in a sequence of empirical evaluations, where our approach is shown to perform almost as well as their optimal parameter settings tuned for individual datasets, while consuming only a fraction of the processing time.