Sejma CicakUmut AvciCicak, SejmaAvci, Umut2025-10-062023979835033752510.1109/HORA58378.2023.101567992-s2.0-85165686674https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165686674&doi=10.1109%2FHORA58378.2023.10156799&partnerID=40&md5=1680314bc14547e17528c52bca18fc35https://gcris.yasar.edu.tr/handle/123456789/8546https://doi.org/10.1109/HORA58378.2023.10156799Imbalanced data is a common problem in many areas and it can have significant impacts on the performance and generalizability of machine learning models. This is because the models fail to create a good representation of the examples in the minority class. This study aims at improving the classification success for the predictive maintenance tasks in which the data is generally imbalanced. To this end we use resampling methods that target creating balanced data. We present various oversampling and undersampling techniques and apply them to both synthetic and real-world datasets. We then perform classification experiments with imbalanced and balanced datasets by using different classifiers. The performances of different classifiers have been compared. More importantly we evaluate the effectiveness of resampling techniques to provide insights into their usefulness in handling class imbalance. Our study contributes to the growing body of literature on addressing the class imbalance in classification tasks and provides practical guidance for selecting appropriate sampling methods based on the characteristics of the dataset. © 2023 Elsevier B.V. All rights reserved.Englishinfo:eu-repo/semantics/closedAccessClassification, Imbalanced Data, Predictive Maintenance, Data Handling, Maintenance, Class Imbalance, Imbalanced Data, Machine Learning Models, Maintenance Tasks, Over Sampling, Performance, Predictive Maintenance, Resampling, Resampling Method, Under-sampling, Classification (of Information)Data handling, Maintenance, Class imbalance, Imbalanced data, Machine learning models, Maintenance tasks, Over sampling, Performance, Predictive maintenance, Resampling, Resampling method, Under-sampling, Classification (of information)ClassificationImbalanced DataPredictive MaintenanceConference Object