Material classification from imprecise chemical composition : probabilistic vs possibilistic approach

Arnaud Grivet Sébert (1) , Jean-Philippe Poli (1)

1 LI3A - Intelligence Artificielle et Apprentissage Automatique

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.

Keywords: possibility theory; clustering algorithm; chemical analysis; chemical engineering computing; fuzzy set theory; pattern classification; pattern clustering; imprecise chemical composition; possibilistic approach; probabilistic approach; possibility; probability; fuzzy rules; fuzzy decision tree; imprecise data; explainable material classification; classification; fuzzy logic; artificial intelligence; online learning; machine learning

Domains: Computer Science [cs]; Artificial Intelligence [cs.AI]; Computer Science [cs]; Machine Learning [cs.LG]; Engineering Sciences [physics]; Materials

Arnaud Grivet Sébert, Jean-Philippe Poli. Material classification from imprecise chemical composition : probabilistic vs possibilistic approach. 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Jul 2018, Rio de Janeiro, Brazil. pp.8491485, ⟨10.1109/FUZZ-IEEE.2018.8491485⟩. ⟨cea-01992290⟩