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
Complete list of metadatas

Cited literature [15 references]  Display  Hide  Download
https://hal-cea.archives-ouvertes.fr/cea-01992290
Contributor : Marie-France Robbe <>
Submitted on : Friday, February 1, 2019 - 10:19:50 AM
Last modification on : Wednesday, September 4, 2019 - 1:40:15 PM
Long-term archiving on: Thursday, May 2, 2019 - 2:13:44 PM

File

article_JPPoli_CBordMaterialCl...
Files produced by the author(s)

Identifiers

Collections

CEA | DRT | CEA-UPSAY | UNIV-PARIS-SACLAY | LIST | DM2I

Citation

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⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

70

Files downloads

118

Contact

CCSD