Material Classification from Imprecise Chemical Composition : Probabilistic vs Possibilistic Approach

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...

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Bibliographic Details
Published in2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) pp. 1 - 8
Main Authors Sebert, Arnaud Grivet, Poli, Jean-Philippe
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2018
Subjects
Chemicals
clustering
Clustering algorithms
Containers
Decision trees
explainable material classification
fuzzy decision tree
fuzzy rules
imprecise data
Linguistics
Neutrons
possibility
Probabilistic logic
probability
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Summary: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.
DOI:10.1109/FUZZ-IEEE.2018.8491485