{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T10:55:19Z","timestamp":1774695319357,"version":"3.50.1"},"reference-count":49,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T00:00:00Z","timestamp":1774656000000},"content-version":"vor","delay-in-days":86,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:p>Nowadays, the management and analysis of big data have become major challenges for researchers in the field of data mining. The increasing rate of data generation, along with the need to extract meaningful patterns, highlights the necessity of developing scalable big data analysis methods. In this context, fuzzy rule\u2010based models have emerged as powerful tools for knowledge extraction from data. However, designing these models typically requires the monolithic use of the entire dataset, which is impractical for big data scenarios due to computational limitations. This study introduces a novel concept and proposes a new framework for designing and evaluating the performance of rule\u2010based models in big data environments. Within this framework, a set of rule\u2010based submodels are randomly constructed using sampled data and trained through Bagging. The rules extracted from these submodels are then aggregated using an optimization\u2010based weighting strategy combined with an information entropy method. This approach, which has not yet been explored in the literature, contributes to improving model efficiency. In the experimental section, large\u2010scale datasets with high dimensionality and volume are employed to comprehensively evaluate the performance of the proposed model. The results demonstrate that the proposed model achieves significant improvements over comparable models.<\/jats:p>","DOI":"10.1155\/cplx\/3937849","type":"journal-article","created":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T10:38:46Z","timestamp":1774694326000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Design of a TSK Rule\u2010Based Model With Granular Rules and Ensemble Learning in Big Data"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7947-5364","authenticated-orcid":false,"given":"Mohammad","family":"Nematpour","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9215-6015","authenticated-orcid":false,"given":"Farnaz","family":"Mahan","sequence":"additional","affiliation":[]},{"given":"Witold","family":"Pedrycz","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7595-8350","authenticated-orcid":false,"given":"Habib","family":"Izadkhah","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2026,3,28]]},"reference":[{"key":"e_1_2_11_1_2","article-title":"Harnessing the Power of Big Data: Challenges and Opportunities in Analytics","volume":"44","author":"Kumar N.","year":"2023","journal-title":"Tuijin Jishu\/Journal of Propulsion Technology"},{"key":"e_1_2_11_2_2","volume-title":"Tensor Methods in High Dimensional Data Analysis: Opportunities and Challenges","author":"Auddy A.","year":"2024"},{"key":"e_1_2_11_3_2","first-page":"78","article-title":"The Management of Data Quality Assessment in Big Data Presents a Complex Challenge, Accompanied by Various Issues Related to Data Quality","volume":"8","author":"Shanmugam D.","year":"2023","journal-title":"Research Highlights in Mathematics and Computer Science"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/encyclopedia4040108"},{"key":"e_1_2_11_5_2","volume-title":"Neural-Based Classification Rule Learning for Sequential Data","author":"Collery M.","year":"2023"},{"key":"e_1_2_11_6_2","first-page":"580","volume-title":"IEEE","author":"Rout S.","year":"2023"},{"key":"e_1_2_11_7_2","doi-asserted-by":"crossref","unstructured":"PullisseryY. 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