Journal of System Simulation ›› 2024, Vol. 36 ›› Issue (5): 1189-1198.doi: 10.16182/j.issn1004731x.joss.22-1550

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Tri-training Algorithm Based on Density Peaks Clustering

Luo Yuhang1(), Wu Runxiu1, Cui Zhihua2, Zhang Yiying3, He Yeshen4, Zhao Jia1()   

  1. 1.School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China
    2.College of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan 030024, China
    3.College of Artificial Intelligence, Tianjin University of Science & Technology, Tianjin 300457, China
    4.China Gridcom Co. , Ltd. , Shenzhen 518000, China
  • Received:2022-12-29 Revised:2023-03-11 Online:2024-05-15 Published:2024-05-21
  • Contact: Zhao Jia;


Tri-training can effectively improve the generalization ability of classifiers by using unlabeled data for classification, but it is prone to mislabeling unlabeled data, thus forming training noise. Tri-training (Tri-training with density peaks clustering, DPC-TT) algorithm based on density peaks clustering is proposed. The DPC-TT algorithm uses the density peaks clustering algorithm to obtain the class cluster centers and local densities of the training data, and the samples within the truncation distance of the class cluster centers are identified as the samples with better spatial structure, and these samples are labeled as the core data, and the classifier is updated with the core data, which can reduce the training noise during the iteration to improve the performance of the classifier. The experimental results show that the DPC-TT algorithm has better classification performance compared with the standard Tri-training algorithm and its improvement algorithm.

Key words: Tri-training, semi-supervised learning, density peaks clustering, spatial structure, classifier

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