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Classifying rockburst with confidence:A novel conformal prediction approach

International Journal of Mining Science and Technology 2024年第1期34卷 51-64页

作者： Bemah Ibrahim Isaac Ahenkorah University of South Australia UniSA STEMSA 5000Australia

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“confident”classifications 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.

关键词： Rockburst Machine learning Uncertainty quantification conformal prediction

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Predicting Amazon customer reviews with deep confidence using deep learning and conformal prediction

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Journal of Management Analytics 2022年第1期9卷 1-16页

作者： Ulf Norinder Petra Norinder Department of Computer and Systems Sciences Stockholm UniversityKistaSweden Stockholm Business School Stockholm UniversityStockholmSweden

In this investigation,we have shown that the combination of deep learning,including natural language processing,and conformal prediction results in highly predictive and efficient temporal test set sentiment estimates for 12 categories of Amazon product reviews using either in-category predictions,*** model and the test set are from the same review category or cross-category predictions,*** a model of another review category for predicting the test *** similar results from in-and cross-category predictions indicate high degree of generalizability across product review *** investigation also shows that the combination of deep learning and conformal prediction gracefully handles class imbalances without explicit class balancing measures.

关键词： Amazon customer reviews machine learning conformal prediction deep learning natural language processing temporal test sets

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