A survey on imbalanced learning: latest research, applications and future directions

• DOI: 10.1007/s10462-024-10759-6
• Publication Date: 2024-05-09T08:01:51Z
• Authors: Wuxing Chen; Kaixiang Yang; Zhiwen Yu; Yifan Shi; C. L. Philip Chen
• Keywords: Realm

Abstract Imbalanced learning constitutes one of the most formidable challenges within data mining and machine learning. Despite continuous research advancement over past decades, from with an imbalanced class distribution remains a compelling area. distributions commonly constrain practical utility even deep models in tangible applications. Numerous recent studies have made substantial progress field learning, deepening our understanding its nature while concurrently unearthing new challenges. Given field’s rapid evolution, this paper aims to encapsulate breakthroughs by providing in-depth review extant strategies confront issue. Unlike surveys that primarily address classification tasks we also delve into techniques addressing regression facets long-tail Furthermore, explore real-world applications devising broad spectrum management science engineering, lastly, discuss newly-emerging issues necessitating further exploration realm