Extending FSNPC to handle data points with fuzzy class assignments

Abstract. In this paper we present an advanced Nearest Prototype Classification to handle data points with unsharp class assignments. Therefore we extend the Soft Nearest Prototype Classification as proposed by Seo et al. and its further enhancement working with fuzzy labeled prototypes as introduced by Villmann et al. We adapt the cost function and derive appropriate update rules for the prototypes. We assess the performance on a toy data set and a real-world problem and compare the classification result with the results obtained by Fuzzy Robust Soft LVQ by means of Fuzzy Cohen’s Kappa.