Graph-Based Class-Imbalance Learning With Label Enhancement | IEEE Journals & Magazine | IEEE Xplore

Graph-Based Class-Imbalance Learning With Label Enhancement


Abstract:

Class imbalance is a common issue in the community of machine learning and data mining. The class-imbalance distribution can make most classical classification algorithms...Show More

Abstract:

Class imbalance is a common issue in the community of machine learning and data mining. The class-imbalance distribution can make most classical classification algorithms neglect the significance of the minority class and tend toward the majority class. In this article, we propose a label enhancement method to solve the class-imbalance problem in a graph manner, which estimates the numerical label and trains the inductive model simultaneously. It gives a new perspective on the class-imbalance learning based on the numerical label rather than the original logical label. We also present an iterative optimization algorithm and analyze the computation complexity and its convergence. To demonstrate the superiority of the proposed method, several single-label and multilabel datasets are applied in the experiments. The experimental results show that the proposed method achieves a promising performance and outperforms some state-of-the-art single-label and multilabel class-imbalance learning methods.
Published in: IEEE Transactions on Neural Networks and Learning Systems ( Volume: 34, Issue: 9, September 2023)
Page(s): 6081 - 6095
Date of Publication: 20 December 2021

ISSN Information:

PubMed ID: 34928806

Funding Agency:


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References

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