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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References
Akiyama, T., Inokuchi, H.: Application of fuzzy decision tree to analyze the attitude of citizens for wellness city development. In: 2016 17th International Symposium on Advanced Intelligent Systems. IEEE, August 2016
Aupetit, M., Allano, L., Espagnon, I., Sannié, G.: Visual analytics to check marine containers in the eritr@c project. In: Proceedings of International Symposium on Visual Analytics Science and Technology, pp. 3–4 (2010)
Bounhas, M., Prade, H., Serrurier, M., Mellouli, K.: A possibilistic rule-based classifier. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds.) IPMU 2012. CCIS, vol. 297, pp. 21–31. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31709-5_3
Chang, R.L.P., Pavlidis, T.: Fuzzy decision tree algorithms. IEEE Trans. Syst. Man Cybern. 7(1), 28–35 (1977)
Coppi, R., D’Urso, P.: Fuzzy time arrays and dissimilarity measures for fuzzy time trajectories. In: Kiers, H.A.L., Rasson, J.P., Groenen, P.J.F., Schader, M. (eds.) Data Analysis, Classification, and Related Methods, pp. 273–278. Springer, Heidelberg (2000). https://doi.org/10.1007/978-3-642-59789-3_44
Duch, W.: Uncertainty of data, fuzzy membership functions, and multilayer perceptrons. IEEE Trans. Neural Netw. 16(1), 10–23 (2005)
D’Urso, P., de Giovanni, L.: Robust clustering of imprecise data. Chemometr. Intell. Lab. Syst. 136, 58–80 (2014)
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)
Gullo, F., Ponti, G., Tagarelli, A.: Clustering uncertain data via K-medoids. In: Greco, S., Lukasiewicz, T. (eds.) SUM 2008. LNCS (LNAI), vol. 5291, pp. 229–242. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87993-0_19
Ishibuchi, H., Nakasima, T., Morisawa, T.: Voting in fuzzy rule-based systems for pattern classification problems. Fuzzy Sets Syst. 103, 223–238 (1999)
Janikow, C.Z.: Fuzzy decision trees: issues and methods. IEEE Trans. Syst. Man Cybern. - Part B: Cybern. 28(1), 1–14 (1998)
Kaufman, L., Rousseeuw, P.J.: Clustering by means of medoids. In: Dodge, Y. (ed.) Statistical data analysis based on the \( L_{1} \) norm and related methods, pp. 405–416 (1987)
Olaru, C., Wehenkel, L.: A complete fuzzy decision tree technique. Fuzzy Sets Syst. 138, 221–254 (2003)
Peng, Y., Flach, P.A.: Soft discretization to enhance the continuous decision tree induction. Integrating Aspects of Data Mining, Decision Support and Meta-Learning (2001)
Tsang, S., Kao, B., Yip, K., Ho, W., Lee, S.: Decision trees for uncertain data. IEEE Trans. Knowl. Data Eng. (1), 64–78 (2011)
Acknowledgment
This research has been funded by the project H2020 C-BORD. We warmly thank S. Moretto, C. Fontana, F. Pino, A. Sardet, C. Carasco, B. Pérot and V. Picaud for their contributions before our work, for their availability and their expertise.
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Grivet Sébert, A., Poli, JP. (2018). Fuzzy Rule Learning for Material Classification from Imprecise Data. In: Medina, J., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018. Communications in Computer and Information Science, vol 853. Springer, Cham. https://doi.org/10.1007/978-3-319-91473-2_6
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DOI: https://doi.org/10.1007/978-3-319-91473-2_6
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