Three-Way Co-Training with Pseudo Labels for Semi-Supervised Learning

Liuxin Wang, Can Gao (), Jie Zhou and Jiajun Wen
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Liuxin Wang: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
Can Gao: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
Jie Zhou: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
Jiajun Wen: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China

Mathematics, 2023, vol. 11, issue 15, 1-21

Abstract: The theory of three-way decision has been widely utilized across various disciplines and fields as an efficient method for both knowledge reasoning and decision making. However, the application of the three-way decision theory to partially labeled data has received relatively less attention. In this study, we propose a semi-supervised co-training model based on the three-way decision and pseudo labels. We first present a simple yet effective method for producing two views by assigning pseudo labels to unlabeled data, based on which a heuristic attribute reduction algorithm is developed. The three-way decision is then combined with the concept of entropy to form co-decision rules for classifying unlabeled data into useful, uncertain, or useless samples. Finally, some useful samples are iteratively selected to improve the performance of the co-decision model. The experimental results on UCI datasets demonstrate that the proposed model outperforms other semi-supervised models, exhibiting its potential for partially labeled data.

Keywords: three-way decision; co-training; pseudo labels; normalized entropy; partially labeled data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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