Application of Semi-Supervised Learning Model to Coal Sample Classification

Abstract

As an extremely important energy source, improving the efficiency and accuracy of coal classification is important for industrial production and pollution reduction. Laser-induced breakdown spectroscopy (LIBS) is a new technology for coal classification which has the ability to rapidly analyze coal compared with traditional coal analysis methods. In the practical application of LIBS, a large amount of labeling data is usually required, but it is quite difficult to obtain labeling data in industrial sites. In this paper, to address the problem of insufficient labeled data, a semi-supervised classification model (SGAN) based on adversarial neural network is proposed, which can utilize unlabeled data to improve the classification accuracy. The effects of labeled and unlabeled samples on the classification accuracy of the SGAN model are investigated, and the results show that the number of labeled and unlabeled samples are positively correlated, and the highest average classification accuracy that the model can achieve is 98.5%. In addition, the classification accuracies of SGAN and other models (e.g., CNN, RF) are also compared, and the results show that, with the same number of labeled samples in the three models, SGAN performs better after the number of unlabeled samples reaches a certain level, with an improvement of 0.7% and 2.5% compared to the CNN and RF models, respectively. This study provides new ideas for the application of semi-supervised learning in LIBS.