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In particular, if an FRBC selects the class related to the rule with the highest activation (so-called Winner-Takes-All approach), then fuzziness, as quantified by the slope of the membership functions, has no impact in classifying data in regions of the input space where rules dominate. On the other hand, fuzziness affects the behaviour of the FRBC in regions where the confidence in classification is low. As a consequence, in the context of Explainable Artificial Intelligence, fuzziness is profitable in FRBCs only if classification is accompanied by an explanation of the confidence of the provided outputs.\n<\/jats:p>","DOI":"10.1007\/s00500-022-07128-2","type":"journal-article","created":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T04:02:35Z","timestamp":1651809755000},"page":"6519-6527","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Effect of fuzziness in fuzzy rule-based classifiers defined by strong fuzzy partitions and winner-takes-all inference"],"prefix":"10.1007","volume":"26","author":[{"given":"Gabriella","family":"Casalino","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giovanna","family":"Castellano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8471-5403","authenticated-orcid":false,"given":"Ciro","family":"Castiello","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Corrado","family":"Mencar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,5,6]]},"reference":[{"key":"7128_CR1","doi-asserted-by":"crossref","unstructured":"Alonso J, Conde-Clemente P, Trivino G (2017) Linguistic description of complex phenomena With The rLDCP R package. 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