Imbalanced Data in Machine Learning: A Comprehensive Review

Abstract

Imbalanced data is a common challenge in machine learning where the distribution of classes in a dataset is skewed, with one class significantly outnumbering the others. This phenomenon can lead to biased models and reduced performance, posing a substantial problem in various real-world applications. This paper provides a comprehensive review of imbalanced data in machine learning, addressing the causes, consequences, and strategies for tackling this issue. We delve into various techniques, algorithms, and best practices that have been proposed to mitigate the challenges posed by imbalanced data, with a focus on their advantages, limitations, and practical considerations. This review aims to offer a comprehensive resource for researchers and practitioners working with imbalanced datasets, fostering a deeper understanding of the subject and guiding the selection of appropriate methodologies to address this pervasive problem.