Material Classification from Imprecise Chemical Composition : Probabilistic vs Possibilistic Approach

Arnaud Grivet Sébert; Jean-Philippe Poli

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.

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

Document type :

Conference papers

Domain :

Computer Science [cs] / Artificial Intelligence [cs.AI]

Computer Science [cs] / Machine Learning [cs.LG]

Engineering Sciences [physics] / Materials

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https://hal-cea.archives-ouvertes.fr/cea-01992290
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Submitted on : Friday, February 1, 2019 - 10:19:50 AM
Last modification on : Monday, May 13, 2019 - 11:15:33 AM
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