Journal
INFORMATION SCIENCES
Volume 544, Issue -, Pages 500-518Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2020.08.104
Keywords
Three-way decision; Semi-supervised reduct; Confidence discernibility matrix; Co-decision; Partially labeled data
Categories
Funding
- National Natural Science Foundation of China [61806127, 62076164, 61703283, 61976145]
- Natural Science Foundation of Guangdong Province, China [2018A030310451, 2018A030310450]
- Shenzhen Institute of Artificial Intelligence and Robotics for Society
- Bureau of Education of Foshan [2019XJZZ05]
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The paper introduces a three-way co-decision model for partially labeled data, focusing on semi-supervised attribute reduction algorithms and classification of unlabeled data to improve model performance.
The theory of three-way decision plays an important role in decision making and knowledge reasoning. However, little attention has been paid to the problem of learning from partially labeled data with three-way decision. In this paper, we propose a three-way codecision model for partially labeled data. More specifically, the problem of attribute reduction for partially labeled data is first investigated, and two semi-supervised attribute reduction algorithms based on novel confidence discernibility matrix are proposed. Then, a three-way co-decision model is introduced to classify unlabeled data into useful, useless, and uncertain data, and the model is iteratively retrained on the carefully selected useful data to improve its performance. Moreover, we theoretically analyze the effectiveness of the proposed model. The experimental results conducted on UCI data sets demonstrate that the proposed model is promising, and even compares favourably with the single supervised classifier trained on all training data with true labels. (C) 2020 Elsevier Inc. All rights reserved.
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