Distinction Sensitive Learning Vector Quantisation-a new noise-insensitive classification method

A Distinction Sensitive Learning Vector Quantizer (DSLVQ), based on the LVQ3 algorithm, is introduced which automatically adjusts the influence of the input features according to their observed relevance for classification. DSLVQ is less sensitive to noisy features than standard LVQ and its importance adjustments are transparent and can be exploited for input data feature selection. As an example, the algorithm is applied to the classification of two artificial data sets: Breiman´s (1984) waveform data and Kohonen´s “hard” classification task