Material Classification from Imprecise Chemical Composition : Probabilistic vs Possibilistic Approach

Arnaud Grivet Sébert 1 Jean-Philippe Poli 1
1 LADIS - Laboratoire d'analyse des données et d'intelligence des systèmes
DM2I - Département Métrologie Instrumentation & Information : DRT/LIST/DM2I
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.
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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⟩

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