Material Classification from Imprecise Chemical Composition : Probabilistic vs Possibilistic Approach

Arnaud Grivet Sébert, Jean-Philippe Poli

Abstract: In this paper we propose a method of explainable material classification from imprecise chemical compositions. The problem of classification from imprecise data is addressed with a fuzzy decision tree whose terms are learned by a clustering algorithm. We deduce fuzzy rules from the tree, which will provide a justification of the result of the classification. Two opposed approaches are compared : the probabilistic approach and the possibilistic approach.