# 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. semi-supervised learning; laser-induced breakdown spectrum; classification of coal samples; generating adversarial neural networks +
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the measurement accuracy of ash, volatile matter, fixed carbon, and calorific value met the requirements of neutron-activated coal online analyzer. Meanwhile, [PERSON] et al. [5] also successfully developed a LIBS online inspection system, which realized automatic sampling and analysis, and could complete elemental analysis, coal quality analysis, and calorific value analysis. The relative error of elemental analysis was within 10%, while the relative error of ash analysis ranged from 2.29% to 13.47%. In addition to its application in the field of coal quality analysis, LIBS technology is also widely used in soil [6], metal [7], food [8], and other fields, showing its wide applicability in different fields. Classification has been a topic of great interest in the field of LIBS, and people have been trying to find ways to improve the classification accuracy, and the commonly used classification algorithms for LIBS are Partial Least Squares Discriminant Analysis (PLS-DA), Random Forests (RF), Support Vector Machines (SVMs), KNN, and Neural Networks, and in addition to these classical machine learning algorithms, researchers have also proposed many special algorithms based on different principles. [PERSON] et al. [9] applied the PLS-DA algorithm to the identification of rock types on the surface of Mars and found that the PLS-DA algorithm has good sensitivity in the task of classifying Martian rocks. [PERSON] et al. [10] used the RF algorithm to classify the spectra of pulsed rocks and coal in order to separate the detritus from the coal, and improved the classification accuracy from 98.30% to 99.96%. [PERSON] et al. [11] combined PCA with SVM to build a PCA-SVM model and optimized the model parameters using a grid search method, which ultimately achieved a classification accuracy of 98.52% on the test set. [PERSON] et al. [12] utilized a genetic algorithm (GA)-optimized support vector machine (SVM) to perform the classification, followed by regression prediction of each type separately, which significantly reduced the RMSECV of the test set. [PERSON] et al. [13] used KNN algorithm to classify coal with an accuracy of 97.73%. [PERSON] et al.