基于PCA，CBLOF和SVMSMOTE算法组合的岩爆烈度等级分级预测

doi:10.3724/1000-6915.jrme.2024.0475

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摘要为降低岩爆样本数据中特征参数的相关性、剔除各类数据中的离群点和平衡数据集中各类岩爆等级的数量，提升岩爆数据库质量，提出一种PCA(principal component analysis)，CBLOF(cluster-based local outlier factor)和SVMSMOTE(support vector machine synthetic minority over-sampling technique)组合算法的方法。首先收集国内外343组岩爆案例构建原始岩爆数据集，使用PCA降维方法对原始岩爆数据集样本进行降维处理，CBLOF算法剔除各类岩爆等级中的离群点，SVMSMOTE算法在各岩爆等级边界附近合成新的少数类样本，最后将处理后的岩爆数据库和原始岩爆数据库分别加入6种机器学习算法模型进行训练，验证PCA，CBLOF和SVMSMOTE算法组合的有效性。结果表明：经过PCA，CBLOF和SVMSMOTE算法组合处理后的数据库所训练的AdaBoost，CatBoost，LightGBM，GradientBoosting，ExtraTrees和RandomForest算法模型的准确率，相比于原始岩爆数据库样本所训练的算法模型准确率分别高29%，28.5%，34%，28%，26.5%，24%。因此，基于PCA，CBLOF和SVMSMOTE组合算法对岩爆数据库进行数据处理，能有效提升岩爆数据库质量，改善机器学习预测模型的性能。

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	姚囝1
	2
	张义礼1
	刘洋1
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	叶义成1
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	骆效兵3
	冯杰3
	黄兆云4

关键词 ：岩石力学, PCA算法, CBLOF算法, SVMSMOTE算法, 岩爆, 数据处理, 机器学习

Abstract：To reduce the correlation among feature parameters in rock burst sample data，eliminate outliers and balance the number of various rock burst levels in the dataset，a method combining principal component analysis (PCA)，cluster-based local outlier factor(CBLOF) and support vector machine synthetic minority over-sampling technique(SVMSMOTE) is proposed to enhance the quality of the rock burst database. Initially，a total of 343 rock burst cases are collected from both domestic and international sources to build the original dataset. PCA is employed for dimensionality reduction，CBLOF is used to identify and eliminate outliers within each rock burst level，and SVMSMOTE synthesizes new minority samples near the boundaries of each rock burst level. The processed and original rock burst databases are subsequently used to train six different machine learning models to validate the effectiveness of the PCA，CBLOF，and SVMSMOTE combination. The results show that the accuracy of models trained on the processed database significantly improved：AdaBoost by 29%，CatBoost by 28.5%，LightGBM by 34%，Gradient Boosting by 28%，ExtraTrees by 26.5% and Random Forest by 24%，compared to models trained on the original database. Therefore，processing the rock burst database using the combined PCA，CBLOF and SVMSMOTE algorithms effectively enhances the quality of the database and im-proves the performance of machine learning prediction models.

Key words： rock mechanics PCA algorithm CBLOF algorithm SVMSMOTE algorithm rock burst data processing machine learning

引用本文:

姚囝1，2，张义礼1，刘洋1，2，叶义成1，2，骆效兵3，冯杰3，黄兆云4. 基于PCA，CBLOF和SVMSMOTE算法组合的岩爆烈度等级分级预测[J]. 岩石力学与工程学报, 2025, 44(5): 1230-1241.
YAO Nan1，2，ZHANG Yili1，LIU Yang1，2，YE Yicheng1，2，LUO Xiaobing3，FENG Jie3，HUANG Zhaoyun4. Rock burst intensity grading prediction based on the combination of PCA，CBLOF and SVMSMOTE algorithms. , 2025, 44(5): 1230-1241.

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https://rockmech.whrsm.ac.cn/CN/10.3724/1000-6915.jrme.2024.0475 或 https://rockmech.whrsm.ac.cn/CN/Y2025/V44/I5/1230

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