Rejection strategies for learning vector quantization
Lydia J. Fischer, Barbara Hammer, Heiko Wersing · 2014
Abstract. We present prototype-based classification schemes, e. g. learn-ing vector quantization, with cost-function-based and geometrically mo-tivated reject options. We evaluate the reject schemes in experiments on artificial and benchmark data sets. We demonstrate that reject options improve the accuracy of the models in most cases, and that the perfor-mance of the proposed schemes is comparable to the optimal reject option of the Bayes classifier in cases where the latter is available. 1