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Conference papers
Material classification from imprecise chemical composition : probabilistic vs possibilistic approach
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
classification
fuzzy logic
artificial intelligence
online learning
machine learning
Cited literature [15 references]
https://hal-cea.archives-ouvertes.fr/cea-01992290
Contributor : Marie-France Robbe Connect in order to contact the contributor
Submitted on : Friday, February 1, 2019 - 10:19:50 AM
Last modification on : Monday, April 4, 2022 - 12:27:59 PM
Long-term archiving on: : Thursday, May 2, 2019 - 2:13:44 PM
Contributor : Marie-France Robbe Connect in order to contact the contributor
Submitted on : Friday, February 1, 2019 - 10:19:50 AM
Last modification on : Monday, April 4, 2022 - 12:27:59 PM
Long-term archiving on: : Thursday, May 2, 2019 - 2:13:44 PM
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