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Inspired by the self\u2010organisation map, Self\u2010Organised Granular encoding has been shown to be effective for generating reliable discrete data clustering results as it is a data encoding technique that uses fuzzy sets and granularity to handle uncertain and imprecise information within discrete data. However, it is mainly useful for unsupervised learning, and its feasibility for supervised learning has not yet been studied. Also, discrete data classification is still under\u2010researched. This paper proposes a new discrete data classification method called Transposed Fuzzy Class Granular classification. This method aims to transform discrete data into fuzzy partitions by considering all available classes and generating representations of the trained class's Transposed Fuzzy Class Granular distribution by measuring the total divergence from the average of each fuzzy class's membership degree distribution. The paper introduces a novel approach to discrete data classification by adapting class granules for classification and improving performance by tackling uncertainty, ambiguity, and the unique characteristics of discrete datasets. The study examined seven discrete datasets and compared their performance with eight commonly used classifiers as the baseline. These datasets were naturally discrete or created by discrete partitions of real datasets. The experimental results demonstrate that the proposed classifier outperforms the baseline classifiers in discrete data classification.<\/jats:p>","DOI":"10.1111\/exsy.70288","type":"journal-article","created":{"date-parts":[[2026,5,28]],"date-time":"2026-05-28T09:47:27Z","timestamp":1779961647000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Discover Class\u2010Based Feature Distribution by Encoding Discrete Data for Classification"],"prefix":"10.1111","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2615-6356","authenticated-orcid":false,"given":"Qiang","family":"Fu","sequence":"first","affiliation":[{"name":"School of Computer Science Queensland University of Technology  Brisbane Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3594-8980","authenticated-orcid":false,"given":"Yuefeng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science Queensland University of Technology  Brisbane Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohui","family":"Tao","sequence":"additional","affiliation":[{"name":"University of Southern Queensland  Springfield Campus Queensland Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianming","family":"Yong","sequence":"additional","affiliation":[{"name":"University of Southern Queensland  Springfield Campus Queensland Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,28]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0377-2217(03)00002-X"},{"issue":"1","key":"e_1_2_10_3_1","first-page":"31","article-title":"A Survey on Multi Criteria Decision Making Methods and Its Applications","volume":"1","author":"Aruldoss M.","year":"2013","journal-title":"American Journal of Information Systems"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-29127-2"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.2174\/138920209789208228"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2021.3069221"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.112617"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TETCI.2024.3360336"},{"key":"e_1_2_10_9_1","unstructured":"Dorffner G.1992.\u201c\u2018Winner\u2010Take\u2010More\u2019: A Mechanism for Soft Competitive Learning.\u201d \u00d6sterreichisches Forschungsinstitut f\u00fcr Artificial Intelligence."},{"key":"e_1_2_10_10_1","volume-title":"Fundamentals of Fuzzy Sets","author":"Dubois D.","year":"2012"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40168-020-00934-6"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-025-02576-2"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41066-023-00370-5"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TENCON.2019.8929617"},{"key":"e_1_2_10_15_1","unstructured":"Hu Z. andL. 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