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

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