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...

Cijeli opis

Bibliografski detalji

Glavni autori:	Zakarya Farou, Yizhi Wang, Tomáš Horváth
Format:	Artigo
Jezik:	Inglês
Izdano:	Elsevier 2024-06-01
Serija:	Intelligent Systems with Applications
Teme:	Class imbalance Adaptive oversampling Clustering analysis Predicting benchmark k-Nearest neighbor
Online pristup:	http://www.sciencedirect.com/science/article/pii/S2667305324000334
Oznake:	Dodaj oznaku Bez oznaka, Budi prvi tko označuje ovaj zapis!