An Improved Oversampling Algorithms based on Informative Sample Selection Strategy Solving Imbalance
Pages 236 - 244
Abstract
Imbalanced data has been the focus of ongoing classification research. It describes a scenario where the distribution of data samples is uneven, and one or more classes in the dataset are underrepresented as a result. When trained on such datasets, this mismatch has a negative impact on the performance of conventional learning models. The key problem is in finding appropriate samples for creating synthetic data, even though numerous strategies have been developed to overcome class imbalance during data pre-processing. In this study, we offer an efficient method for overcoming imbalance classification issues caused by oversampling called Informative Sample Selection (ISS). The main goal of ISS is to find useful samples from the minority class in the dataset that may be used to produce data that is synthetic. We conducted experiments on 22 imbalanced datasets to evaluate the performance of our suggested model. We assessed the performance of ISS in comparison to several cutting-edge techniques, including SMOTE, Borderline-SMOTE, ADASYN, safe-level SMOTE, and ROS. AUC and F-Measure were the evaluation measures employed in our study. The outcomes of our tests show that ISS works better than the current approaches, showing significant progress in tackling the challenges brought on by imbalanced data in classification.
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Published In
August 2023
783 pages
ISBN:9798400700224
DOI:10.1145/3607947
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Published: 28 September 2023
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IC3 2023
IC3 2023: 2023 Fifteenth International Conference on Contemporary Computing
August 3 - 5, 2023
Noida, India
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