A Systematic Review on Imbalanced Data Challenges in Machine Learning

By Harsurinder Kaur, Husanbir Singh Pannu, Avleen Kaur Malhi. ACM Computing Surveys, 2019

683 citations recorded by the source corpus.

In machine learning, the data imbalance imposes challenges to perform data analytics in almost all areas of real-world research. The raw primary data often suffers from the skewed perspective of data distribution of one class over the other as in the case of computer vision, information security, marketing, and medical science. The goal of this article is to present a comparative analysis of the approaches from the reference of data pre-processing, algorithmic and hybrid paradigms for contemporary imbalance data analysis techniques, and their comparative study in lieu of different data distribution and their application areas.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Health Information Management

View Paper: https://www.semanticscholar.org/paper/7ac8f533a18f584387dd412a0a27feb9af1c5c93

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Citations in the corpus

Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022, background): “Often mentioned factors contributing to over ﬁ tting are a small sample size in combination with many candidate features, perfect separation on rare categories, and a large imbalance resulting in a small number of events for one of the outcomes 6,47,49,72,76,77,82 .”
A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning (2023, neutral): “Handling unbalanced data has been extensively studied in the literature (Japkowicz & Stephen, 2002; Kamalov et al., 2022; Wang et al., 2018; Haixiang et al., 2017; Wang et al., 2021; Kaur et al., 2019).”