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Towards instance-dependent label noise-tolerant classification: a probabilistic approach

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Abstract

Learning from labelled data is becoming more and more challenging due to inherent imperfection of training labels. Existing label noise-tolerant learning machines were primarily designed to tackle class-conditional noise which occurs at random, independently from input instances. However, relatively less attention was given to a more general type of label noise which is influenced by input features. In this paper, we try to address the problem of learning a classifier in the presence of instance-dependent label noise by developing a novel label noise model which is expected to capture the variation of label noise rate within a class. This is accomplished by adopting a probability density function of a mixture of Gaussians to approximate the label flipping probabilities. Experimental results demonstrate the effectiveness of the proposed method over existing approaches.

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Notes

  1. We used LIBLINEAR [31] in this study.

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Acknowledgements

The authors would like to thank anonymous reviewers for constructive comments. This research is financially supported by the Thailand Research Fund (Grant No. MRG59080235). Department of Computer Science, Faculty of Science at Chiang Mai University provides research and computing facilities.

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  1. Department of Computer Science, Chiang Mai University, Chiang Mai, 50200, Thailand

    Jakramate Bootkrajang & Jeerayut Chaijaruwanich

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  1. Jakramate Bootkrajang
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  2. Jeerayut Chaijaruwanich
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Correspondence to Jakramate Bootkrajang.

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Bootkrajang, J., Chaijaruwanich, J. Towards instance-dependent label noise-tolerant classification: a probabilistic approach. Pattern Anal Applic 23, 95–111 (2020). https://doi.org/10.1007/s10044-018-0750-z

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