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Research on Multi-Label Semi-Supervised Learning Algorithm Based on Dual Selection Criteria
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AI
DOI：10.1109/ACCESS.2024.3369919.png)
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摘要
En 中文
With the rapid development of information technology, efficient multi label classification of massive data is one of the important tasks of big data systems. Semi supervised learning algorithm is an effective data classification method, currently mainly applied to the classification of single label data. This article proposes a multi label dynamic semi supervised learning algorithm based on dual selection criteria. The algorithm mainly establishes dual selection criteria for multi label pseudo labeled samples based on the COIN structure and K-nearest neighbor algorithm. A novel pseudo labeled sample selection method is designed, which improves the robustness and accuracy of the algorithm and effectively solves the problem of not considering sample correlation when selecting pseudo labeled samples. On this basis, by adding a performance evaluation mechanism to the model, the model can dynamically and adaptively extract pseudo labeled samples, improving the training speed and accuracy of the model. This article selected four convincing public test datasets for experiments, and the experimental results showed that the proposed semi supervised learning method has improved in multiple indicators such as robustness, accuracy, and training efficiency compared to current mainstream methods.
Keyword:
Training
Data models
Adaptation models
Semisupervised learning
Resource management
Prediction algorithms
Iterative methods
Labeling
Nearest neighbor methods
Information technology
Multi-label
semi-supervised learning
COIN structure
K-nearest neighbor algorithm
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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