Median Variants of LVQ for Optimization of Statistical Quality Measures for Classification of Dissimilarity Data

We consider in this article median variants of the learning vector quantization classi-fier for classification of dissimilarity data. particularly we are interested in optimiza-tion of advanced classification quality measures like sensitivity, specificity or the Fβ-measure. These measures are frequently more appropriate than simple accuracy, in particular, if the training data are imbalanced for the investigated data classes. We present the mathematical theory for this approach based on a generalized Expectation-Maximization-scheme