What does this research mean for the field?

Imbalanced data distributions significantly hinder the performance of machine learning models by biasing decision-making processes. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to systematically analyze the challenges posed by imbalanced data in machine learning and categorize existing solutions.

March 8, 2026Frontiers of Computer ScienceOpen Access

A comprehensive survey on imbalanced data learning

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Authors

XGXinyi GaoThe University of Queensland DXDongting XieYunnan University YZYihang ZhangGuangxi University

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Implication

Survey analyzes imbalanced data learning, categorizing solutions and highlighting future challenges in ML applications.

Key Points

This research aims to systematically analyze the challenges posed by imbalanced data in machine learning and categorize existing solutions.
Categorization of existing research into four areas: data re-balancing, feature representation, training strategy, and ensemble learning.
Review of various real-world data formats and their impact on machine learning performance.
Overview of open-source libraries and identification of current challenges in imbalanced data learning.
Identified key challenges related to imbalanced data that hinder machine learning performance.
Provided insights into four distinct categories of solutions for addressing imbalanced data.
Offered guidance for future research directions and advancements in imbalanced data learning.

Cite This Study

Gao et al. (2026) studied this question.

synapsesocial.com/papers/69ada804bc08abd80d5bb1cd https://doi.org/10.1007/s11704-025-50274-7

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A comprehensive survey on imbalanced data learning

Discussion

Member takes

Implication

Key Points

Cite This Study

A comprehensive survey on imbalanced data learning

Discussion

Member takes

Implication

Key Points

Cite This Study

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