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

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May 9, 2024
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Research Abstract & Technology Focus

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.
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What is the core focus of the research titled 'A survey on imbalanced learning: latest research, applications and future directions'?

This literature focuses on: AbstractImbalanced learning constitutes one of the most formidable challenges within data mining and machine learning. Despite continuous research advancement over the past decades, learning from data with an imbalanced class distribution remains ...

Are there open-source GitHub repositories related to A survey on imbalanced learning: latest research, applications and future directions?

Yes, open-source projects like THU-MAIC/OpenMAIC (Open Multi-Agent Interactive Classroom — Get an immersive, multi-agent learning experience in just one click) are actively building upon these concepts.

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Yes, highly correlated activity was mapped. An entry titled 'A survey on imbalanced learning: latest research, applications and future directions' discusses this: AbstractImbalanced learning constitutes one of the most formidable challenges within data mining and machine learning. Despite continuous research ...

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