{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T07:53:52Z","timestamp":1768204432066,"version":"3.49.0"},"reference-count":50,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T00:00:00Z","timestamp":1764720000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Instance selection is an important data preprocessing step in data mining that removes noisy data from training datasets to build more effective models and reduces dataset size to improve computational efficiency during model training. Traditional instance selection algorithms have varying strengths and weaknesses, making it difficult for any single method to consistently achieve optimal performance across diverse datasets. Therefore, in this paper, two novel ensemble instance selection methods are proposed, namely Single\u2010Stage Parallel Ensemble (SSPE) and Two\u2010Stage Serial Ensemble (TSSE), which are based on combining multiple instance selection algorithms in the parallel and serial manners. The proposed ensemble approaches aim to improve data reduction and classification performance. Extensive experiments are conducted on 17 medical datasets of varying sizes, evaluating four instance selection algorithms, CNN, ENN, IPF, and GA, alongside three classifiers: KNN, SVM, and RF. Results demonstrate that certain SSPE combinations, particularly the union of ENN and IPF, outperform single baseline algorithms in classification accuracy and AUC while maintaining effective data reduction. Although TSSE achieves higher reduction rates, its classification performance is inferior to SSPE. Overall, the proposed methods serve as effective preprocessing tools for medical data and provide a strong baseline for future ensemble instance selection research.<\/jats:p>","DOI":"10.1002\/cpe.70478","type":"journal-article","created":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T10:44:28Z","timestamp":1764758668000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Ensemble Instance Selection for Medical Datasets: Single\u2010Stage Parallel and Two\u2010Stage Serial Ensemble Approaches"],"prefix":"10.1002","volume":"38","author":[{"given":"Min\u2010Wei","family":"Huang","sequence":"first","affiliation":[{"name":"School of Medicine, Kaohsiung Medical University  Kaohsiung Taiwan"},{"name":"Kaohsiung Municipal Kai\u2010Syuan Psychiatric Hospital  Kaohsiung Taiwan"},{"name":"Department of Physical Therapy and Graduate Institute of Rehabilitation Science China Medical University  Taichung Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5991-2253","authenticated-orcid":false,"given":"Chih\u2010Fong","family":"Tsai","sequence":"additional","affiliation":[{"name":"Department of Information Management National Central University  Taoyuan Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wun\u2010Sin","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Information Management National Central University  Taoyuan 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