Research on Multi-Label Semi-Supervised Learning Algorithm Based on Dual Selection Criteria

Abstract

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.