# Research on Hard Rock Pillar Stability Prediction Based on SABO-LSSVM Model
## 5 Conclusions  
After optimization using the SABO algorithm, the average prediction accuracy of the LSSVM model reached 83.75%. Compared to the conventional LSSVM, SVM, and ELM models under the same conditions, it improved by 18.99%, 30.32%, and 16.78%, respectively, demonstrating excellent performance in predicting the state of the pillar samples. Compared with other optimization algorithms, the SABO algorithm showed better overall optimization performance for the LSSVM prediction model. When using different feature combinations, the SABO-LSSVM model generally outperformed the other algorithms in terms of prediction accuracy. From the cumulative distribution curves, it can be observed that the SABO-optimized model had a higher likelihood of achieving high accuracy (above 90%) and higher numerical values of prediction accuracy, indicating that the model had superior generalization and stability compared with other optimization models. In this study, seven pillar feature indicators, including pillar width (W), pillar height (H), pillar aspect ratio (K), average pillar constraint (C\({}_{\text{pav}}\)), uniaxial compressive strength of rock (UCS), pillar stress (\(\sigma\)), and pillar strength (\(\sigma_{\text{p}}\)), were selected as input values for training the model, and redundant feature analysis and selection were conducted. From the performance of different datasets in various models, the M-3 feature combination performed the best, which is consistent with the results cited in [2; 14]. This indicates that it is necessary to consider the pillar width (W) and pillar height (H) during feature selection, and not treat them as redundant features to be removed. In conclusion, the optimized prediction model used in this study performed best when using the M-3 feature combination as the dataset, which included W, H, K, UCS, and \(\sigma\). To further investigate the impact of data balancing methods on the SABO-LSSVM model, this study employed two commonly used oversampling methods, Smote and Adasyn, and the Tomek-Smote method improved by the Tomek Links. The distribution plots of prediction results and evaluation metrics, such as F1 scores, showed that the Smote and Adasyn oversampling algorithms could improve the overall prediction accuracy of the model but introduced interpolated samples with noisy information, leading to misclassification of certain samples. The Tomek-Smote sampling method effectively reduced the probability of misclassification while improving the overall accuracy of the model. In summary, the SABO-LSSVM model demonstrated the advantages of a simple initial setup and good predictive performance in this study, providing assistance in predicting the stability state of pillars. However, the model also has issues, such as fast convergence and susceptibility to local optima, which may require the further introduction of new methods for improvement. Machine learning methods provide a new perspective and tool for pillar stability analysis, but they still rely on the experience and effective information summarized from methods currently used in the actual mining industry. When introducing these emerging methods, it is crucial to objectively and reasonably select datasets that can reflect the essence of the problem. With the development of machine learning technology, high-performance, simple, and efficient prediction models and techniques will emerge in the future. Improvement strategies may include, but are not limited to, refining the evaluation indicator system for pillar stability, constructing effective pillar sample datasets, and developing new data mining and application technologies. Methodology, H.Z. ; Software, H.Z. ; Formal analysis, H.Z. ; Writing--original draft, H.Z. ; Supervision, X.X. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.