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In this paper, we propose new methods and evaluate how their core component, the distance mapping, can be adapted to multi-label learning. The proposed approach is based on combining the distance mapping with an inverse distance weighting. Although the proposal is one of the simplest methods in the multi-label learning literature, it achieves state-of-the-art performance for small to moderate-sized multi-label learning problems. In addition to its simplicity, the proposed method is fully deterministic: Its hyper-parameter can be selected via ranking loss-based statistic which has a closed form, thus avoiding conventional cross-validation-based hyper-parameter tuning. In addition, due to its simple linear distance mapping-based construction, we demonstrate that the proposed method can assess the uncertainty of the predictions for multi-label classification, which is a valuable capability for data-centric machine learning pipelines.<\/jats:p>","DOI":"10.1007\/s10994-025-06923-w","type":"journal-article","created":{"date-parts":[[2025,11,25]],"date-time":"2025-11-25T21:52:11Z","timestamp":1764107531000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Minimal learning machine for multi-label learning"],"prefix":"10.1007","volume":"114","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8466-9232","authenticated-orcid":false,"given":"Joonas","family":"H\u00e4m\u00e4l\u00e4inen","sequence":"first","affiliation":[]},{"given":"Antoine","family":"Hubermont","sequence":"additional","affiliation":[]},{"given":"Amauri","family":"Souza","sequence":"additional","affiliation":[]},{"given":"C\u00e9sar L. 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