Material classification from imprecise chemical composition: probabilistic vs possibilistic approach
Published in 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2018
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.
Recommended citation: Grivet Sébert, A., & Poli, J. P. (2018, July). Material classification from imprecise chemical composition: probabilistic vs possibilistic approach. In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-8). IEEE.
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