{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T21:02:30Z","timestamp":1771016550469,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T00:00:00Z","timestamp":1761264000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Science Foundation","award":["450627322"],"award-info":[{"award-number":["450627322"]}]},{"DOI":"10.13039\/501100002347","name":"BMFTR","doi-asserted-by":"publisher","award":["01ZZ2316N"],"award-info":[{"award-number":["01ZZ2316N"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002347","name":"BMFTR","doi-asserted-by":"publisher","award":["CALM-QE 01ZZ2318I"],"award-info":[{"award-number":["CALM-QE 01ZZ2318I"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The classification performance of an inference model trained in a supervised manner depends substantially on the size and quality of the labeled training data. The characteristics of the underlying data distribution significantly impact the generalization ability of a trained model, particularly in cases where some class overlap can be observed. In such cases, training a single model on the entirety of the labeled data can result in an increase in the complexity of the resulting decision boundary, leading to over-fitting and consequently to some poor generalization performance. In the current work, a cluster-based sample weighting approach is proposed in order to improve the generalization ability of a classification model while dealing with such complex data distributions. The approach consists of first performing a clustering of the training data and subsequently optimizing cluster-specific classification models, using a weighted loss based on the samples-to-cluster-center distances. An unseen sample is first assigned a cluster and subsequently classified based on the model specific to its assigned cluster. The proposed approach was evaluated on three different pain recognition datasets, and the performed evaluation showed that the approach is not only able to attain state-of-the-art classification performances but also systematically outperforms its single model counterpart.<\/jats:p>","DOI":"10.3390\/computers14110457","type":"journal-article","created":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T02:34:45Z","timestamp":1761273285000},"page":"457","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dealing with Class Overlap Through Cluster-Based Sample Weighting"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6769-8410","authenticated-orcid":false,"given":"Patrick","family":"Thiam","sequence":"first","affiliation":[{"name":"Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, 89069 Ulm, Germany"},{"name":"Institute of Neural Information Processing, Ulm University, James-Franck-Ring, 89081 Ulm, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5118-0812","authenticated-orcid":false,"given":"Friedhelm","family":"Schwenker","sequence":"additional","affiliation":[{"name":"Institute of Neural Information Processing, Ulm University, James-Franck-Ring, 89081 Ulm, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4759-5254","authenticated-orcid":false,"given":"Hans Armin","family":"Kestler","sequence":"additional","affiliation":[{"name":"Institute of Medical Systems Biology, Ulm University, Albert-Einstein-Allee 11, 89069 Ulm, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106631","DOI":"10.1016\/j.knosys.2020.106631","article-title":"On the Class Overlap Problem in Imbalanced Data Classification","volume":"212","author":"Vuttipittayamongkol","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"109721","DOI":"10.1016\/j.patcog.2023.109721","article-title":"Addressing the Class-Imbalance and Class-Overlap Problems by a Metaheuristic-based Under-Sampling Approach","volume":"143","author":"Soltanzadeh","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.inffus.2022.08.017","article-title":"A Unifying View of Class Overlap and Imbalance: Key Concepts, Multi-View Paranoma, and Open Avenues for Research","volume":"89","author":"Santos","year":"2023","journal-title":"Inf. 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