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• W3048507955 abstract "Abstract An imbalanced dataset is commonly found in at least one class, which are typically exceeded by the other ones. A machine learning algorithm (classifier) trained with an imbalanced dataset predicts the majority class (frequently occurring) more than the other minority classes (rarely occurring). Training with an imbalanced dataset poses challenges for classifiers; however, applying suitable techniques for reducing class imbalance issues can enhance classifiers’ performance. In this study, we consider an imbalanced dataset from an educational context. Initially, we examine all shortcomings regarding the classification of an imbalanced dataset. Then, we apply data-level algorithms for class balancing and compare the performance of classifiers. The performance of the classifiers is measured using the underlying information in their confusion matrices, such as accuracy, precision, recall, and F measure. The results show that classification with an imbalanced dataset may produce high accuracy but low precision and recall for the minority class. The analysis confirms that undersampling and oversampling are effective for balancing datasets, but the latter dominates." @default.
• W3048507955 created "2020-08-18" @default.
• W3048507955 creator A5040400767 @default.
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• W3048507955 date "2020-08-11" @default.
• W3048507955 modified "2023-09-30" @default.
• W3048507955 title "Conversion of adverse data corpus to shrewd output using sampling metrics" @default.
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• W3048507955 doi "https://doi.org/10.1186/s42492-020-00055-9" @default.
• W3048507955 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7417470" @default.
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• W3048507955 hasPublicationYear "2020" @default.
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