Fusion of one-class classifiers in the belief function framework

Abstract

A method is proposed for converting a novelty measure such as produced by one-class SVMs or Kernel principal component analysis (KPCA) into a belief function on a well- defined frame of discernment. This makes it possible to combine one-class classification or novelty detection methods with other information expressed in the same framework such as expert opinions or multi-class classifiers.