A rejection-based possibilistic classifier and its parameters learning
Carl Frélicot · 2002
This paper presents a rejection-based and class-selective possibilistic classifier and its parameters learning. The classifier is defined as a couple of functions (D,T), D being a labelling one and T being a hardening one in a non-exclusive way. The parameters of the classifier (D,T), whose strategy for rejection is not classical, are learned using a suitable clustering algorithm and statistical operators. We illustrate the proposed method on both artificial noisy data and real data.