A novel features partition algorithm for semi-supervised categorization

Tang, Huan; Lin, Zheng Kui; Lu, Mingyu; Liu, Na

doi:10.1109/wcica.2008.4592911

Cited by 1 publication

(1 citation statement)

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“…X. Wei proposed a Reliable Label Selection and Learning (ReLSL) algorithm to solve the problem of semi supervised deep learning when there is only a very small amount of labeled image data [14]. H. Tang proposed a u-wordMixup method for data augmentation of unlabeled samples, which solves the quality problems that may arise from unlabeled and annotated samples coming from different fields, and improves generalization ability and accuracy [15]. Z. Wu proposed a Conditional Consistency Regularization (CCR) tailored for Semi Supervised Single Label Image Classification (SS-SLC), which encourages two predictions to remain consistent and establishes a relationship between given two different label states, which helps to utilize label relationships to promote image classification [16].…”

Section: Introductionmentioning

confidence: 99%

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

Liu,

Lu,

Shi

et al. 2024

IEEE Access

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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.

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Section: Introductionmentioning

confidence: 99%