Sparse conformal prediction for dissimilarity data

Abstract

Existing classification algorithms focus on vectorial data given in Euclidean space or representations by means of positive semi-definite kernel matrices. Many real world data, like biological sequences are not vectorial, often non-euclidean and given only in the form of (dis-)similarities between examples, requesting for efficient and interpretable models. Vectorial embeddings or transformations to get a valid kernel are limited and current dissimilarity classifiers often lead to dense complex models which are hard to interpret by domain experts. They also fail to provide additional information about the confidence of the classification. In this paper we propose a prototype-based conformal classifier for dissimilarity data. It is based on a prototype dissimilarity learner and extended by the conformal prediction methodology. It (i) can deal with dissimilarity data characterized by an arbitrary symmetric dissimilarity matrix, (ii) offers intuitive classification in terms of sparse prototypical class representatives, (iii) leads to state-of-the-art classification results supported by a confidence measure and (iv) the model complexity is automatically adjusted. In experiments on dissimilarity data we investigate the effectiveness with respect to accuracy and model complexity in comparison to different state of the art classifiers.