We study multiclass classification methods, whereby the problem is reduced to a single binary classifier (SBC). Such SBC reductions are obtained by embedding the original problem in a higher dimensional space consisting of the original features, as well as several other dimensions determined by a set of (error correcting) codewords. The outstanding features of these methods are their operational simplicity and competitive classification performance. We examine several known and new SBC reductions and provide a comprehensive study of their empirical performance. We also consider a subsampling heuristic that can decrease the computational cost of SBC methods, without significantly reducing classification accuracy. We conclude that SBC approaches are an attractive alternative to standard multiclass decompositions.
By: Ran El-Yaniv; Tomer Kotek; Dmitry Pechyony; Elad Yom-Tov
Published in: H-0241 in 2006
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