Oversampling With Reliably Expanding Minority Class Regions for Imbalanced Data Learning
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2022.3171706
Abstract: This paper proposes a simple interpolation Oversampling method with the purpose of Reliably Expanding the Minority class regions (OREM). OREM first finds the candidate minority region around each original minority sample, then exploits this region… read more here.
Keywords: class; expanding minority; class regions; minority class ... See more keywords
Embedding Undersampling Rotation Forest for Imbalanced Problem
Sign Up to like & getrecommendations! Published in 2018 at "Computational Intelligence and Neuroscience"
DOI: 10.1155/2018/6798042
Abstract: Rotation Forest is an ensemble learning approach achieving better performance comparing to Bagging and Boosting through building accurate and diverse classifiers using rotated feature space. However, like other conventional classifiers, Rotation Forest does not work… read more here.
Keywords: undersampling rotation; class; rotation forest; embedding undersampling ... See more keywords