Imbalanced classification: A paradigm-based review

Yang Feng, Min Zhou, Xin Tong

Research output: Contribution to journalArticlepeer-review


A common issue for classification in scientific research and industry is the existence of imbalanced classes. When sample sizes of different classes are imbalanced in training data, naively implementing a classification method often leads to unsatisfactory prediction results on test data. Multiple resampling techniques have been proposed to address the class imbalance issues. Yet, there is no general guidance on when to use each technique. In this article, we provide a paradigm-based review of the common resampling techniques for binary classification under imbalanced class sizes. The paradigms we consider include the classical paradigm that minimizes the overall classification error, the cost-sensitive learning paradigm that minimizes a cost-adjusted weighted type I and type II errors, and the Neyman–Pearson paradigm that minimizes the type II error subject to a type I error constraint. Under each paradigm, we investigate the combination of the resampling techniques and a few state-of-the-art classification methods. For each pair of resampling techniques and classification methods, we use simulation studies and a real dataset on credit card fraud to study the performance under different evaluation metrics. From these extensive numerical experiments, we demonstrate under each classification paradigm, the complex dynamics among resampling techniques, base classification methods, evaluation metrics, and imbalance ratios. We also summarize a few takeaway messages regarding the choices of resampling techniques and base classification methods, which could be helpful for practitioners.

Original languageEnglish (US)
Pages (from-to)383-406
Number of pages24
JournalStatistical Analysis and Data Mining
Issue number5
StatePublished - Oct 2021


  • Neyman–Pearson (NP) paradigm
  • binary classification
  • classical classification (CC) paradigm
  • cost-sensitive (CS) learning paradigm
  • imbalance ratio
  • imbalanced data
  • resampling methods

ASJC Scopus subject areas

  • Analysis
  • Information Systems
  • Computer Science Applications


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