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Noise cleaning for nonuniform ordinal labels based on inter-class distance

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Abstract

Label noise poses a significant challenge to supervised learning algorithms. Extensive research has been conducted on classification and regression tasks, but label noise filtering methods specifically designed for ordinal regression are lacking. In this paper, we propose a set of ordinal label noise filtering frameworks by theoretically exploring the generalization error bound in noisy environments. Besides, we present a robust label noise estimation method voted by inter-class distance. It takes into account the nonuniformity of ordinal labels and the reliability of the base model. This estimator is integrated into our framework in the proposed Inter-Class Distance-based Filtering (ICDF) algorithm. We empirically demonstrate the effectiveness of ICDF in identifying label noise and achieving improved generalization performance. Our experiments conducted on benchmark and real age estimation datasets show the superiority of ICDF over the existing filters in ordinal label noise cleaning.

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Data availability and access

The benchmark ordinal regression datasets are available at http://www.gatsby.ucl.ac.uk/~chuwei/ordinalregression.html. The Adience age estimation dataset is provided at https://talhassner.github.io/home/projects/Adience/Adience-data.html.

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Funding

This work was supported by the National Natural Science Foundation of China (62276161, U21A20513, 62076154, 61906113), and the Key R &D Program of Shanxi Province (202202020101003, 202302010101007)

Author information

Authors and Affiliations

  1. School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China

    Gaoxia Jiang, Fei Wang & Wenjian Wang

  2. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, 030006, China

    Wenjian Wang

Authors
  1. Gaoxia Jiang
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  2. Fei Wang
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  3. Wenjian Wang
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Contributions

All authors contributed to the algorithm conception and design. Theoretical analysis was completed by Gaoxia Jiang and Wenjian Wang. Data collection and analysis were performed by Fei Wang. The first draft of the manuscript was written by Gaoxia Jiang. All authors commented on previous versions of the manuscript and approved the final version.

Corresponding author

Correspondence to Gaoxia Jiang.

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Ethical and informed consent for data used

The benchmark ordinal regression datasets are open source. The Adience age estimation dataset will be made available upon reasonable request.

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The authors have no relevant financial or nonfinancial interests to disclose.

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Jiang, G., Wang, F. & Wang, W. Noise cleaning for nonuniform ordinal labels based on inter-class distance. Appl Intell 54, 6997–7011 (2024). https://doi.org/10.1007/s10489-024-05551-6

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  • DOI: https://doi.org/10.1007/s10489-024-05551-6

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