# Application of Semi-Supervised Learning Model to Coal Sample Classification
###### Abstract
[14] used K-Means to cluster coal, municipal sludge, and biomass samples, and then used SVM to further classify the biomass samples; the combined accuracy of the hybrid classification model reaches more than 98%, and it saves more time for the running time. [PERSON] et al. [15] developed a PCA-ANN model to classify iron ore samples, and, after parameter optimization, the model achieved 99.19% classification accuracy on the test set. [PERSON] et al. [16] used wavelet neural network (WNN) to classify coal ash, and the results showed that WNN showed better classification performance. [PERSON] et al. [17] used CNN, combined with multi-task regularization, proposed a transfer learning method which significantly reduced the RMSEP compared with the baseline method. [PERSON] et al. [18] proposed a moisture-spectral intensity correction model, and established an ANN model to improve the model prediction capability. The plain Bayesian classification method is able to distinguish coal samples into different origins based on the probability distribution function. [PERSON] et al. [19] used this method to classify coal samples with a prediction accuracy of 96.7%. In addition to these classical machine learning algorithms, researchers have proposed many different methods, such as Piecewise Modeling [20] and Multiple-setting Spectra [21]. Based on the above discussion, it is clear that fully supervised learning has achieved good research results in LIBS. In the practical use of LIBS technology, we found that the acquisition of LIBS spectra is simple, but quantitative analysis for it requires a large amount of known data to build quantitative regression models, and the acquisition of labeled data is not easy in many domains, e.g., computer vision [22], Natural Language Processing (NLP) [23], medical image analysis [24], bioinformatics [25], the food [26], and other fields. Therefore, there is an urgent need to develop algorithms that can classify LIBS spectra in semi-supervised scenarios. [PERSON] et al. [27] proposed a semi-supervised learning algorithm based on the KNN algorithm and the extended algorithm for the problem of classification of explosives, where a small amount of labeled data and a large amount of unlabeled data are used as the input variables, and after computation, all data will obtain their respective final labels. After testing, the accuracy of the model can reach 99.58%. [PERSON] et al. [28] proposed a semi-supervised LIBS quantitative analysis method based on least squares support vector machine (LS-SVM) co-training regression model, and the prediction of Cr concentration in the high-alloy steel specimens showed that using the co-training technique and adding effective unlabeled specimens during the training process can effectively improve the performance of the regression model. [PERSON] et al. [29] proposed a semi-supervised learning (SSL) classification model based on supervised linear discriminant analysis (LDA) and semi-supervised support vector machine (OC-SVM), which is able to classify known minerals and detect unknown substances in the sequence set. [PERSON] et al. [30] proposed a semi-supervised Gene-SGAN algorithm, which dissects disease heterogeneity by jointly considering phenotypic and genetic data to confer genetic correlation between disease subtypes and associated endophenotypic features. [PERSON] et al. [31] proposed a STFT-SACGAN-based bearing fault diagnosis method, which was validated and found to be a model with excellent fault recognition ability, and alleviated the problem of scarce labeled data. In this paper, the semi-supervised learning model SGAN is applied to LIBS spectral classification to address the problem of the lack of labeled data that may occur in future LIBS applications. The advantage of the semi-supervised learning model is that only a small number of labeled samples, combined with a large number of unlabeled samples, can effectively improve the classification accuracy of the model.