Cluster-based oversampling with area extraction from representative points for class imbalance learning

Class imbalance learning is challenging in various domains where training datasets exhibit disproportionate samples in a specific class. Resampling methods have been used to adjust the class distribution, but they often have limitations for small disjunct minority subsets. This paper introduces AROS...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Zakarya Farou, Yizhi Wang, Tomáš Horváth
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	Elsevier 2024-06-01
Sarja:	Intelligent Systems with Applications
Aiheet:	Class imbalance Adaptive oversampling Clustering analysis Predicting benchmark k-Nearest neighbor
Linkit:	http://www.sciencedirect.com/science/article/pii/S2667305324000334
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