Abstract
The paper addresses the structure of fuzzy rule-based classifiers from the point of view of a function relating membership grades of inputs with rule outputs and a discriminant function. Rule-based level set based models are employed to produce classifiers using data to find the output functions of the fuzzy rules. A simple and effective formulation consists of estimating the parameters of the output function of each rule using input and output class data. The data driven method gives an easy and efficient mechanism to produce rule-based fuzzy classifiers. Performance evaluation is done using the data sets and classifiers available in scikit-learn, currently a reference in machine learning. The results suggest that data driven level set fuzzy classifiers performance competes closely or surpasses state of the art classifiers.
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Acknowledgements
The first author is grateful to the Brazilian National Council for Scientific and Technological Development for grant 302467/2019-0. The authors also tank the reviewers for their helpful comments.
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Gomide, F., Yager, R. (2023). Data Driven Level Set Fuzzy Classification. In: Cohen, K., Ernest, N., Bede, B., Kreinovich, V. (eds) Fuzzy Information Processing 2023. NAFIPS 2023. Lecture Notes in Networks and Systems, vol 751. Springer, Cham. https://doi.org/10.1007/978-3-031-46778-3_4
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DOI: https://doi.org/10.1007/978-3-031-46778-3_4
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