Abstract
Class imbalance all around presents in real-world applications, which has brought more curiosity from different fields. While emphasising on accuracy for performance evaluation, studying from unbalanced data may produce unproductive outcomes. Cost-sensitive, sampling, ensemble approach and other hybrid methodologies have all been used in the past to address this imbalance problem. In machine learning, the ensemble approach is used to increase the accuracy of single base classifiers by aggregating numerous of them. To handle the issues due to imbalanced data, ensemble algorithms have to be formed specifically. Several performance assessing functions showed that the ensemble method outperformed the other techniques. In this article, different methods are described to handle imbalanced datasets with the special description of SMOTE with the ensemble method. The complexity of the ensemble model is defined. The clustering methods are also used to manage the issues due to imbalanced datasets.
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Rout, N., Mishra, D., Mallick, M.K., Mallick, P.K. (2022). Dealing with Imbalanced Data. In: Mallick, P.K., Bhoi, A.K., González-Briones, A., Pattnaik, P.K. (eds) Electronic Systems and Intelligent Computing. Lecture Notes in Electrical Engineering, vol 860. Springer, Singapore. https://doi.org/10.1007/978-981-16-9488-2_35
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DOI: https://doi.org/10.1007/978-981-16-9488-2_35
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