A novel adaptive safe semi-supervised learning framework for pattern extraction and classification

Manifold regularization semi-supervised learning is a powerful graph-based semi-supervised learning method. However, the performance of semi-supervised learning methods based on manifold regularization depends to some extent on the quality of the manifold graph and unlabeled samples. Intuitively speaking, the quality of the graph directly affects the final classification performance of the model. In response to the above problems, this paper first proposed an adaptive safety semi-supervised learning framework. The framework implements the weight assignment of the self-similarity graph during the model learning process. In order to adapt to the learning needs, accelerate the learning speed, and avoid the impact of the curse of dimensionality, the framework also optimizes the features of each sample point through an automatic weighting mechanism to extract effective features and eliminate redundant information in the learning task. In addition, the framework defines an adaptive risk measurement mechanism for the uncertainty and potential risks of unlabeled samples to determine the degree of risk of unlabeled samples. Finally, a new adaptive safe semi-supervised extreme learning machine was proposed. Comprehensive experimental results across various class imbalance scenarios demonstrated that our proposed method outperforms other methods in terms of classification accuracy, and other critical performance metrics.