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Strengths of Fuzzy Techniques in Data Science

Abstract : We show that many existing fuzzy methods for machine learning and data mining contribute to providing solutions to data science challenges, even though statistical approaches are often presented as major tools to cope with big data and modern user expectations of their exploitation. The multiple capacities of fuzzy and related knowledge representation methods make them inescapable to deal with various types of uncertainty inherent in all kinds of data.
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https://hal.sorbonne-universite.fr/hal-01676195
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Submitted on : Friday, January 5, 2018 - 11:31:19 AM
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Bernadette Bouchon-Meunier. Strengths of Fuzzy Techniques in Data Science. Kosheleva, O.; Shary, S.P.; Xiang, G.; Zapatrin, R. Beyond Traditional Probabilistic Data Processing Techniques: Interval, Fuzzy, etc. Methods and Their Applications, 835, Springer, pp.111-119, 2020, Studies in Computational Intelligence, 978-3-030-31041-7. ⟨10.1007/978-3-030-31041-7_6⟩. ⟨hal-01676195⟩

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