Fuzzy rule learning for material classification from imprecise data
Published in Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations: 17th International Conference (IPMU 2018), 2018
To address the problem of illicit substance detection at borders, we propose a complete method for explainable classification of materials. The classification is performed using imaprecise chemical data, which is quite rare in the literature. We follow a two-step workflow based on fuzzy logic induction. Firstly, a clustering approach is used to learn the suitable fuzzy terms of the various linguistic variables. Secondly, we induce rules for a justified classification using a fuzzy decision tree. Both methods are adaptations from classic ones to the case of imprecise data. At the end of the paper, results on simulated data are presented in the expectation of real data.
Recommended citation: Grivet Sébert, A., & Poli, J. P. (2018). Fuzzy rule learning for material classification from imprecise data. In Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations: 17th International Conference, IPMU 2018, Cádiz, Spain, June 11-15, 2018, Proceedings, Part I 17 (pp. 62-73). Springer International Publishing.
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