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However,\u00a0class imbalance\u00a0in\u00a0cyberbullying datasets, where the percentage of\u00a0normal labeled classes\u00a0is higher than that of abnormal labeled ones, presents a significant challenge for classification algorithms. This issue is particularly problematic in two-class datasets, where conventional machine learning methods tend to perform poorly on\u00a0minority class samples\u00a0due to the influence of the majority class. To address this problem, researchers have proposed various oversampling and undersampling techniques. In this paper, we investigate the effectiveness of such techniques in addressing class imbalance in cyberbullying datasets. We conduct an experimental study that involves a\u00a0preprocessing step\u00a0to enhance machine learning algorithm performance. We then examine the impact of\u00a0imbalanced data\u00a0on classification performance for four cyberbullying datasets. To study the classification performance on balanced cyberbullying datasets, we employ four resampling techniques, namely random undersampling,\u00a0random oversampling, SMOTE, and SMOTE\u2009+\u2009TOMEK. We evaluate the impact of each\u00a0rebalancing technique\u00a0on\u00a0classification performance\u00a0using eight well-known classification algorithms. Our findings demonstrate that the performance of\u00a0resampling techniques\u00a0depends on the\u00a0dataset size, imbalance ratio, and classifier used. The conducted experiments proved that there are no techniques that will always perform better the others.<\/jats:p>","DOI":"10.1007\/s00521-023-09084-w","type":"journal-article","created":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T06:01:47Z","timestamp":1699250507000},"page":"1049-1065","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["The effect of rebalancing techniques on the classification performance in cyberbullying datasets"],"prefix":"10.1007","volume":"36","author":[{"given":"Marwa","family":"Khairy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tarek M.","family":"Mahmoud","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1785-1058","authenticated-orcid":false,"given":"Tarek","family":"Abd-El-Hafeez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,6]]},"reference":[{"key":"9084_CR1","doi-asserted-by":"crossref","unstructured":"Abdellatif S, Ben Hassine MA, Ben Yahia S, and Bouzeghoub A, ARCID: a new approach to deal with imbalanced datasets classification,\u201d in SOFSEM 2018: Theory and Practice of Computer Science: 44th International Conference on Current Trends in Theory and Practice of Computer Science, Krems, Austria, January 29-February 2, 2018, Proceedings 44, Springer, 2018, pp. 569\u2013580.","DOI":"10.1007\/978-3-319-73117-9_40"},{"key":"9084_CR2","unstructured":"Ali A, Shamsuddin SM, and Ralescu AL (2015), Classification with class imbalance problem: a review,\u201d Int J Adv. 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