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research-article

Counterfactual-based minority oversampling for imbalanced classification

Authors:

Shanshan Huang,

Ming LiuAuthors Info & Claims

Volume 122, Issue C

https://doi.org/10.1016/j.engappai.2023.106024

Published: 01 June 2023 Publication History

Abstract

A key challenge of oversampling in imbalanced classification is that the generation of new minority samples often neglects the usage of majority classes, resulting in most new minority sampling spreading the whole minority space. In view of this, we present a new oversampling framework based on the counterfactual theory. Our framework introduces a counterfactual objective by leveraging the rich inherent information of majority classes and explicitly perturbing majority samples to generate new samples in the territory of minority space. It can be analytically shown that the new minority samples satisfy the minimum inversion. Therefore, most of them are located near the decision boundary. The empirical evaluation of the six benchmark datasets shows that our approach clearly outperforms the state-of-the-art methods.

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Cited By

Yang FZeng GZhong FXiao PZheng WQiu F(2024)CfExplainerJournal of Systems and Software10.1016/j.jss.2024.112182218:COnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.jss.2024.112182
Tao XGuo XZheng YZhang XChen Z(2023)Self-adaptive oversampling method based on the complexity of minority data in imbalanced datasets classificationKnowledge-Based Systems10.1016/j.knosys.2023.110795277:COnline publication date: 28-Sep-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110795

Index Terms

Counterfactual-based minority oversampling for imbalanced classification
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Engineering Applications of Artificial Intelligence

Engineering Applications of Artificial Intelligence Volume 122, Issue C

Jun 2023

1605 pages

ISSN:0952-1976

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Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 June 2023

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Cited By

Yang FZeng GZhong FXiao PZheng WQiu F(2024)CfExplainerJournal of Systems and Software10.1016/j.jss.2024.112182218:COnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.jss.2024.112182
Tao XGuo XZheng YZhang XChen Z(2023)Self-adaptive oversampling method based on the complexity of minority data in imbalanced datasets classificationKnowledge-Based Systems10.1016/j.knosys.2023.110795277:COnline publication date: 28-Sep-2023
https://dl.acm.org/doi/10.1016/j.knosys.2023.110795

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