Abstract
The paper develops an idea of fuzzy evidential classifiers based on modification of logistic regression model and Dempster–Shafer methodology. The proposed approach is integrating the additional linguistic variable into the classifier. This variable considers different shades of truth for class membership hypotheses and enriches available information for decision-making. It leads to identification of pre-failure states and detecting anomalies, inconsistency, and incorrectness in the initial data. As a result of the research, linguistic log-regression model is shown, and its components are justified. The inference procedure based on the model is illustrated. In the end, a simple example of implementation is also shown.
The work was supported by RFBR grants Nos. 19-07-00263, 19-07-00195, 19-08-00152, 20-07-00100, and 20-37-51002.
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Dolgiy, A., Kovalev, S., Kolodenkova, A., Sukhanov, A. (2021). Logistic-Based Design of Fuzzy Interpretable Classifiers. In: Kovalev, S.M., Kuznetsov, S.O., Panov, A.I. (eds) Artificial Intelligence. RCAI 2021. Lecture Notes in Computer Science(), vol 12948. Springer, Cham. https://doi.org/10.1007/978-3-030-86855-0_19
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