Learning from class-imbalanced data: Review of methods and applications

By Haixiang Guo, Yijing Li, J. Shang, Mingyun Gu, Yuanyue Huang, Gong Bing. Expert systems with applications, 2017

2,094 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/971c56413d65e88cd378586666de16cc49e2dd1d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

In Artificial Intelligence

In Electrical and Electronic Engineering

Citations in the corpus

The Hitchhiker’s Guide to Computational Linguistics in Suicide Prevention (2021, background): “Researchers are therefore advised to implement appropriate ML strategies that take into account this class imbalance problem (e.g., Haixiang et al., 2017), which is an inherent problem to the specific classification task of suicide detection.”
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).”
A systematic study of the class imbalance problem in convolutional neural networks (2017, methodology): “In the previous decade the number of publications on imbalance learning problem started growing rapidly [11, 16].”
A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data (2021, neutral): “rectly identified as minority samples [4], [5].”
A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, background): “Moreover, Haixiang et al. [5] in 2017 reviewed papers published over the past decade and highlighted the broad applicability of rare event detection and imbalanced learning across diverse research domains.”
Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates (2018, neutral): “evaluation models, classifying imbalanced data significantly challenges the traditional classification models [14].”
Unequal-Training for Deep Face Recognition With Long-Tailed Noisy Data (2019, background): “…face recognition is reminiscent of the conventional class imbalance problem that has been comprehensively studied in classical machine learning [2,10], but signiﬁcantly differs from conventional class imbalance problem in two aspects: ﬁrst, the long-tailed data in face recognition is…”
Cross-Validation for Imbalanced Datasets: Avoiding Overoptimistic and Overfitting Approaches [Research Frontier] (2018, background): “60 ieee ComputatioNal iNtelligeNCe magaziNe | November 2018 of balancing class distributions without ruling out potentially critical ma jority examples [8].”
On Cross-Dataset Generalization in Automatic Detection of Online Abuse (2020, background): “A number of techniques have been proposed to address class imbalance in data, including data re-sampling, cost-sensitive learning, and neural network specific learning algorithms (Branco et al., 2016; Haixiang et al., 2017; Johnson and Khoshgoftaar, 2019).”
A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining (2023, methodology): “…rather than synthesized data, to reﬂect the patterns educational researchers are likely to come across in EDM research focused on critical target variables, such as school dropout status and post-secondary enrollment, which may have important consequences for both students and educators [6,7].”
Artificial intelligence, bias and clinical safety (2019, positive): “ML systems perform best when index cases and controls are approximately equal in the training set, 37 and this is not common in medicine.”