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In our previous work fuzzy transforms were used for numerical attribute dependency in data analysis: the multi-dimensional inverse fuzzy transform was used to approximate the regression function. Also here the classification method presented is based on this operator. Strictly speaking, we apply the K-fold cross-validation algorithm for controlling the presence of over-fitting and for estimating the accuracy of the classification model: for each training (resp., testing) subset an iteration process evaluates the best fuzzy partitions of the inputs. Finally, a weighted mean of the multi-dimensional inverse fuzzy transforms calculated for each training subset (resp., testing) is used for data classification. We compare this algorithm on well-known datasets with other five classification methods.<\/jats:p>","DOI":"10.1007\/s12652-021-03336-0","type":"journal-article","created":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T10:03:41Z","timestamp":1624269821000},"page":"2873-2885","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A classification algorithm based on multi-dimensional fuzzy transforms"],"prefix":"10.1007","volume":"13","author":[{"given":"Ferdinando","family":"Di Martino","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4303-2884","authenticated-orcid":false,"given":"Salvatore","family":"Sessa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,21]]},"reference":[{"key":"3336_CR1","doi-asserted-by":"publisher","DOI":"10.1201\/b17320","volume-title":"Data classification: algorithms and application","author":"C Aggarwal","year":"2014","unstructured":"Aggarwal C (2014) Data classification: algorithms and application. 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