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This article introduces the Multiplex Classification Framework, a novel approach developed to tackle these and similar challenges through the integration of problem transformation, ontology engineering, and model ensembling. The framework offers several advantages, including adaptability to any number of classes and logical constraints, an innovative method for managing class imbalance, the elimination of confidence threshold selection, and a modular structure. Two experiments were conducted to compare the performance of conventional classification models with the multiplex approach. Our results demonstrate that the multiplex approach can improve classification performance significantly (up to 10% gain in overall F1 score), particularly in classification problems with a large number of classes and pronounced class imbalances. However, it also has limitations, as it requires a thorough understanding of the problem domain and some experience with ontology engineering, and it involves training multiple models, which can make the whole process more intricate. Overall, this methodology provides a valuable tool for researchers and practitioners dealing with complex classification problems in machine learning.<\/jats:p>","DOI":"10.1177\/15705838251340362","type":"journal-article","created":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T03:04:58Z","timestamp":1748487898000},"page":"199-225","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["The Multiplex Classification Framework: Optimizing Multi-Label Classifiers Through Problem Transformation, Ontology Engineering, and Model Ensembling"],"prefix":"10.1177","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1349-5156","authenticated-orcid":false,"given":"Mauro Andr\u00e9s","family":"Nievas Offidani","sequence":"first","affiliation":[{"name":"Department of Electric and Computer Engineering, Universidad Nacional del Sur, Bah\u00eda Blanca, Argentina"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7788-4612","authenticated-orcid":false,"given":"Facundo","family":"Roffet","sequence":"additional","affiliation":[{"name":"Department of Electric and Computer Engineering, Universidad Nacional del Sur, Bah\u00eda Blanca, Argentina"},{"name":"Institute of Computer Science and Engineering, National Scientific and Technological Research Council of Argentina (CONICET), Argentina"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2727-8374","authenticated-orcid":false,"given":"Claudio Augusto","family":"Delrieux","sequence":"additional","affiliation":[{"name":"Department of Electric and Computer Engineering, Universidad Nacional del Sur, Bah\u00eda Blanca, Argentina"},{"name":"Institute of Computer Science and Engineering, National Scientific and Technological Research Council of Argentina (CONICET), Argentina"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3075-457X","authenticated-orcid":false,"given":"Mar\u00eda Carolina","family":"Gonz\u00e1lez Galtier","sequence":"additional","affiliation":[{"name":"Freelance Healthcare Data Analyst, Argentina"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8851-8602","authenticated-orcid":false,"given":"Marcos Daniel","family":"Zarate","sequence":"additional","affiliation":[{"name":"Centre for the Study of Marine Systems, Centro Nacional Patag\u00f3nico (CENPAT-CONICET), Puerto Madryn, Argentina"}]}],"member":"179","published-online":{"date-parts":[[2025,5,29]]},"reference":[{"key":"e_1_3_4_2_1","first-page":"250","volume-title":"Multi-label classification of film genres based on synopsis using support vector machine, logistic regression and na\u00efve Bayes algorithms2022 6th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)","author":"Akbar J.","year":"2022","unstructured":"Akbar J., Utami E., Yaqin A. 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