Fuzzy One-Class Classification Model Using Contamination Neighborhoods

Abstract

A fuzzy classification model is studied in the paper. It is based on the contaminated (robust) model which produces fuzzy expected risk measures characterizing classification errors. Optimal classification parameters of the models are derived by minimizing the fuzzy expected risk. It is shown that an algorithm for computing the classification parameters is reduced to a set of standard support vector machine tasks with weighted data points. Experimental results with synthetic data illustrate the proposed fuzzy model.