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Rough sets-based tri-trade for partially labeled data

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Abstract

The theory of rough sets is one of the most representative models for handling supervised data entangled with vagueness, impreciseness, or uncertainty. However, little work has been devoted to learning from partially labeled data using rough sets. In this study, a rough sets-based tri-trade model is proposed for partially labeled data. More specifically, a new discernibility matrix that considers both labeled and unlabeled data is first proposed, based on which a beam search-based heuristic algorithm is provided to generate multiple semi-supervised reducts. Then, a tri-trade model using three diverse semi-supervised reducts is developed, in which a data editing technique is embedded to generate reliable pseudo-labels for unlabeled data to improve the tri-trade model. Both theoretical analysis and comparative experiments on the UCI datasets show that the proposed model can effectively utilize unlabeled data to improve generalization performance and compare favorably to other representative methods.

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Data Availability

Data and code are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors would like to thank the Editor-in-Chief, Editor, and anonymous reviewers for their kind help and valuable comments. This work is supported in part by the National Natural Science Foundation of China (Nos. 61806127, 62076164), the Natural Science Foundation of Guangdong Province, China (No. 2021A1515011861), Shenzhen Science and Technology Program (No. JCYJ20210324094601005), and Shenzhen Institute of Artificial Intelligence and Robotics for Society.

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Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, People’s Republic of China

    Ziming Luo, Can Gao & Jie Zhou

  2. Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen, 518060, People’s Republic of China

    Ziming Luo, Can Gao & Jie Zhou

  3. SZU Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, 518060, People’s Republic of China

    Can Gao & Jie Zhou

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  1. Ziming Luo
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  2. Can Gao
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Correspondence to Can Gao.

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Luo, Z., Gao, C. & Zhou, J. Rough sets-based tri-trade for partially labeled data. Appl Intell 53, 17708–17726 (2023). https://doi.org/10.1007/s10489-022-04405-3

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