Dissimilarity-based classification for vectorial representations

General dissimilarity-based learning approaches have been proposed for dissimilarity data sets (Pekalska et al., 2002). They arise in problems in which direct comparisons of objects are made, e.g. by computing pairwise distances between images, spectra, graphs or strings. In this paper, we study under which circumstances such dissimilarity-based techniques can be used for deriving classifiers in feature vector spaces. We show that such classifiers perform comparably or better than the nearest neighbor rule based either on the entire or condensed training set. Moreover, they can be beneficial for highly-overlapping classes and for non-normally distributed data sets, with categorical, mixed or otherwise difficult features