Classifying rockburst with confidence:A novel conformal prediction approach

作     者：Bemah Ibrahim Isaac Ahenkorah Bemah Ibrahim;Isaac Ahenkorah

作者机构：University of South AustraliaUniSA STEMSA 5000Australia 

出 版 物：《International Journal of Mining Science and Technology》 (矿业科学技术学报（英文版）)

年 卷 期：2024年第34卷第1期

页      面：51-64页

核心收录：

学科分类：081901[工学-采矿工程] 0819[工学-矿业工程] 08[工学] 

主　　题：Rockburst Machine learning Uncertainty quantification Conformal prediction 

摘      要：The scientific community recognizes the seriousness of rockbursts and the need for effective mitigation *** literature reports various successful applications of machine learning(ML)models for rockburst assessment;however,a significant question remains unanswered:How reliable are these models,and at what confidence level are classifications made?Typically,ML models output single rockburst grade even in the face of intricate and out-of-distribution samples,without any associated confidence *** the susceptibility of ML models to errors,it becomes imperative to quantify their uncertainty to prevent consequential *** address this issue,we propose a conformal prediction(CP)framework built on traditional ML models(extreme gradient boosting and random forest)to generate valid classifications of rockburst while producing a measure of confidence for its *** proposed framework guarantees marginal coverage and,in most cases,conditional coverage on the test *** CP was evaluated on a rockburst case in the Sanshandao Gold Mine in China,where it achieved high coverage and efficiency at applicable confidence ***,the CP identified several“confidentclassifications from the traditional ML model as unreliable,necessitating expert verification for informed *** proposed framework improves the reliability and accuracy of rockburst assessments,with the potential to bolster user confidence.