Stop Oversampling for Class Imbalance Learning: A Review
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DOI: 10.1109/access.2022.3169512
Abstract: For the last two decades, oversampling has been employed to overcome the challenge of learning from imbalanced datasets. Many approaches to solving this challenge have been offered in the literature. Oversampling, on the other hand,… read more here.
Keywords: stop oversampling; class; oversampling methods; oversampling class ... See more keywords
Generative Oversampling Methods for Handling Imbalanced Data in Software Fault Prediction
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Reliability"
DOI: 10.1109/tr.2022.3158949
Abstract: Imbalanced software fault datasets, having fewer faulty modules than the nonfaulty modules, make accurate fault prediction difficult. It is challenging for software practitioners to handle imbalanced fault data during software fault prediction (SFP). Earlier, several… read more here.
Keywords: software fault; prediction; fault; oversampling methods ... See more keywords