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Researchers seek the improvement of machine learning algorithms in not only accuracy, but also interpretability. Interpretable algorithms allow humans to easily understand the decisions that a machine learning model makes, which is challenging for black box models. Mathematical programming-based classification algorithms have attracted considerable attention due to their ability to effectively compete with leading-edge algorithms in terms of both accuracy and interpretability. Meanwhile, the training of a hyper-box classifier can be mathematically formulated as a Mixed Integer Linear Programming (MILP) model and the predictions combine accuracy and interpretability. In this work, an optimisation-based approach is proposed for multi-class data classification using a hyper-box representation, thus facilitating the extraction of compact IF-THEN rules. The key novelty of our approach lies in the minimisation of the number and length of the generated rules for enhanced interpretability. Through a number of real-world datasets, it is demonstrated that the algorithm exhibits favorable performance when compared to well-known alternatives in terms of prediction accuracy and rule set simplicity.<\/jats:p>","DOI":"10.1007\/s10994-024-06643-7","type":"journal-article","created":{"date-parts":[[2025,2,6]],"date-time":"2025-02-06T17:53:55Z","timestamp":1738864435000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Interpretable optimisation-based approach for hyper-box classification"],"prefix":"10.1007","volume":"114","author":[{"given":"Georgios I.","family":"Liapis","sequence":"first","affiliation":[]},{"given":"Sophia","family":"Tsoka","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4652-6086","authenticated-orcid":false,"given":"Lazaros G.","family":"Papageorgiou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,6]]},"reference":[{"key":"6643_CR1","doi-asserted-by":"publisher","first-page":"1418","DOI":"10.1609\/aaai.v33i01.33011418","volume":"33","author":"S Aghaei","year":"2019","unstructured":"Aghaei, S., Azizi, M. 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