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2018 | OriginalPaper | Chapter

Fuzzy Rule Learning for Material Classification from Imprecise Data

Authors : Arnaud Grivet Sébert, Jean-Philippe Poli

Published in: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations

Publisher: Springer International Publishing

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Abstract

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.

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Title: Fuzzy Rule Learning for Material Classification from Imprecise Data
Authors: Arnaud Grivet Sébert
Jean-Philippe Poli
Publisher: Springer International Publishing
Book: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations
Print ISBN: 978-3-319-91472-5

Electronic ISBN: 978-3-319-91473-2

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-91473-2_6

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