Assessing the Impact of Expert Labelling of Training Data on the Quality of Automatic Classification of Lithological Groups Using Artificial Neural Networks

Assessing the Impact of Expert Labelling of Training Data on the Quality of Automatic Classification of Lithological Groups Using Artificial Neural Networks

Machine learning (ML) methods are nowadays widely used to automate geophysical study. Some of ML algorithms are used to solve lithological classification problems during uranium mining process. One of the key aspects of using classical ML methods is causing data features and estimating their influen...

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Bibliographic Details
Main Authors: Kuchin Yan, Mukhamediev Ravil, Yakunin Kirill, Grundspenkis Janis, Symagulov Adilkhan
Format: Article
Language:English
Published: Sciendo 2020-12-01
Series:Applied Computer Systems
Subjects:
assessment of expert influence
lithology
machine learning
shap
uranium mining
Online Access:https://doi.org/10.2478/acss-2020-0016

Internet

https://doi.org/10.2478/acss-2020-0016

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