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A co-training approach for sequential three-way decisions

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Abstract

In recent years, three-way decisions have received much attention in uncertain decision and cost-sensitive learning communities. However, in many real applications, labeled samples are usually far from sufficient. In this case, it is a reasonable choice to defer the decision rather than make an immediate decision without sufficient supported information, thus it constructs a boundary region. In order to label more available samples, a traditional co-training method employs two classifiers on two complementary views to extend the existing training sets. However, the wrong predictions of new labels may lead to a high misclassification cost, especially when few labeled samples are available. To address this problem, a co-training method is incorporated into three-way decisions, which can label new samples with higher confidence. When we obtain sufficient labeled samples, the non-commitment decisions are directly decided to a positive or a negative region, which finally generates a two-way decisions result. Experiments on several face databases are conducted to validate the effectiveness of the proposed approach.

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Acknowledgements

The authors would like to thank the anonymous reviewers for helpful comments. This work was supported by the National Natural Science Foundation of China (Nos. 71671086, 61876079, 71732003, 61773208), and the National Key Research and Development Program of China (Nos. 2016YFD0702100, 2018YFB1402600).

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Authors and Affiliations

  1. School of Management and Engineering, Nanjing University, Nanjing, 210093, China

    Di Dai, Huaxiong Li & Xianzhong Zhou

  2. Research Center for Novel Technology of Intelligent Equipment, Nanjing University, Nanjing, 210093, China

    Huaxiong Li & Xianzhong Zhou

  3. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Xiuyi Jia

  4. School of Information Engineering, Nanjing Audit University, Nanjing, 211815, China

    Bing Huang

  5. Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural affairs, Nanjing, 210014, China

    Sunning Liang

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  1. Di Dai
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  2. Huaxiong Li
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  3. Xiuyi Jia
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  4. Xianzhong Zhou
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  5. Bing Huang
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  6. Sunning Liang
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Correspondence to Huaxiong Li.

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Dai, D., Li, H., Jia, X. et al. A co-training approach for sequential three-way decisions. Int. J. Mach. Learn. & Cyber. 11, 1129–1139 (2020). https://doi.org/10.1007/s13042-020-01086-7

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  • DOI: https://doi.org/10.1007/s13042-020-01086-7

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