{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T19:36:12Z","timestamp":1778787372368,"version":"3.51.4"},"reference-count":61,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,4,9]],"date-time":"2024-04-09T00:00:00Z","timestamp":1712620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Social Fund (ESF)"},{"name":"the Free State of Saxony, Germany"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Background. The importance of explainable artificial intelligence and machine learning (XAI\/XML) is increasingly being recognized, aiming to understand how information contributes to decisions, the method\u2019s bias, or sensitivity to data pathologies. Efforts are often directed to post hoc explanations of black box models. These approaches add additional sources for errors without resolving their shortcomings. Less effort is directed into the design of intrinsically interpretable approaches. Methods. We introduce an intrinsically interpretable methodology motivated by ensemble learning: the League of Experts (LoE) model. We establish the theoretical framework first and then deduce a modular meta algorithm. In our description, we focus primarily on classification problems. However, LoE applies equally to regression problems. Specific to classification problems, we employ classical decision trees as classifier ensembles as a particular instance. This choice facilitates the derivation of human-understandable decision rules for the underlying classification problem, which results in a derived rule learning system denoted as RuleLoE. Results. In addition to 12 KEEL classification datasets, we employ two standard datasets from particularly relevant domains\u2014medicine and finance\u2014to illustrate the LoE algorithm. The performance of LoE with respect to its accuracy and rule coverage is comparable to common state-of-the-art classification methods. Moreover, LoE delivers a clearly understandable set of decision rules with adjustable complexity, describing the classification problem. Conclusions. LoE is a reliable method for classification and regression problems with an accuracy that seems to be appropriate for situations in which underlying causalities are in the center of interest rather than just accurate predictions or classifications.<\/jats:p>","DOI":"10.3390\/make6020038","type":"journal-article","created":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T03:07:48Z","timestamp":1712718468000},"page":"800-826","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Meta Algorithm for Interpretable Ensemble Learning: The League of Experts"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4306-0769","authenticated-orcid":false,"given":"Richard","family":"Vogel","sequence":"first","affiliation":[{"name":"Media Informatics, University of Applied Sciences Mittweida, 09648 Mittweida, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0682-4284","authenticated-orcid":false,"given":"Tobias","family":"Schlosser","sequence":"additional","affiliation":[{"name":"Media Computing, Chemnitz University of Technology, 09107 Chemnitz, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4243-5343","authenticated-orcid":false,"given":"Robert","family":"Manthey","sequence":"additional","affiliation":[{"name":"Media Informatics, University of Applied Sciences Mittweida, 09648 Mittweida, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0204-8275","authenticated-orcid":false,"given":"Marc","family":"Ritter","sequence":"additional","affiliation":[{"name":"Media Informatics, University of Applied Sciences Mittweida, 09648 Mittweida, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5669-9872","authenticated-orcid":false,"given":"Matthias","family":"Vodel","sequence":"additional","affiliation":[{"name":"Media Informatics, University of Applied Sciences Mittweida, 09648 Mittweida, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9519-2708","authenticated-orcid":false,"given":"Maximilian","family":"Eibl","sequence":"additional","affiliation":[{"name":"Media Informatics, Chemnitz University of Technology, 09107 Chemnitz, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kristan Alexander","family":"Schneider","sequence":"additional","affiliation":[{"name":"Modeling and Simulation, University of Applied Sciences Mittweida, 09648 Mittweida, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Maurer, M., Gerdes, J.C., Lenz, B., and Winner, H. 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