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COMIRE: A Consistence-Based Mislabeled Instances Removal Method.

Xiaokun Pu, Chunguang Li, Hui-Liang Shen

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    This study introduces a novel method to improve neural network classifiers by identifying and removing mislabeled training data. The COnsistence-based Mislabeled Instances REmoval (COMIRE) method effectively enhances classification performance on noisy datasets.

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    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Computer Science

    Background:

    • Training neural network classifiers (NNCs) is hindered by label noise, leading to poor generalization.
    • Mislabeled data significantly misleads NNC training, degrading performance in practical applications.

    Purpose of the Study:

    • To address the challenge of label noise in NNCs by proposing a method for mislabeled instance removal.
    • To enhance the generalization performance of NNCs trained on datasets with noisy labels.

    Main Methods:

    • A COnsistence-based Mislabeled Instances REmoval (COMIRE) method is proposed.
    • COMIRE leverages the differing trends in training loss and prediction uncertainty between correctly and incorrectly labeled instances.
    • An iterative scheme is employed to enhance the separability of correctly and mislabeled data.

    Main Results:

    • COMIRE effectively identifies mislabeled instances within training data.
    • The removal of identified mislabeled instances significantly improves NNC classification performance.
    • Experimental results demonstrate the efficacy of COMIRE in handling label noise.

    Conclusions:

    • The proposed COMIRE method offers an effective solution for mitigating the impact of label noise in NNCs.
    • By removing mislabeled data, significant improvements in classification accuracy can be achieved.
    • COMIRE provides a valuable technique for robust NNC training in real-world scenarios with imperfect labels.