Relevance vector machines using weighted expected squared distance for ore grade estimation with incomplete data

Relevance vector machines using weighted expected squared distance for ore grade estimation with... Accurate ore grade estimation is crucial to mineral resources evaluation and exploration. In this paper, we consider the borehole data collected from the Solwara 1 deposit, where the hydrothermal sulfide ore body is quite complicated with incomplete ore grade values. To solve this estimation problem, the relevance vector machine (RVM) and the expected squared distance (ESD) algorithm are incorporated into one regression model. Moreover, we improve the ESD algorithm by weighting the attributes of the data set and propose the weighted expected squared distance (WESD). In this paper, we uncover the symbiosis characteristics among different elements of the deposits by statistical analysis, which leads to estimating certain metal based on the data of other elements instead of on geographical position. The proposed WESD-RVM features high sparsity and accuracy, as well as the capability of handling incomplete data. Effectiveness of the proposed model is demonstrated by comparing with other estimating algorithms, such as inverse distance weighted method and Kriging algorithm which utilize only geographical spatial coordinates for inputs; extreme learning machine, which is unable to deal with incomplete data; and ordinary ESD based RVM regression model without entropy weighted distance. The experimental results show that the proposed WESD-RVM outperforms other methods with considerable predictive and generalizing ability. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Machine Learning and Cybernetics Springer Journals

Relevance vector machines using weighted expected squared distance for ore grade estimation with incomplete data

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Subject
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Control, Robotics, Mechatronics; Complex Systems; Systems Biology; Pattern Recognition
ISSN
1868-8071
eISSN
1868-808X
D.O.I.
10.1007/s13042-016-0535-x
Publisher site
See Article on Publisher Site

Abstract

Accurate ore grade estimation is crucial to mineral resources evaluation and exploration. In this paper, we consider the borehole data collected from the Solwara 1 deposit, where the hydrothermal sulfide ore body is quite complicated with incomplete ore grade values. To solve this estimation problem, the relevance vector machine (RVM) and the expected squared distance (ESD) algorithm are incorporated into one regression model. Moreover, we improve the ESD algorithm by weighting the attributes of the data set and propose the weighted expected squared distance (WESD). In this paper, we uncover the symbiosis characteristics among different elements of the deposits by statistical analysis, which leads to estimating certain metal based on the data of other elements instead of on geographical position. The proposed WESD-RVM features high sparsity and accuracy, as well as the capability of handling incomplete data. Effectiveness of the proposed model is demonstrated by comparing with other estimating algorithms, such as inverse distance weighted method and Kriging algorithm which utilize only geographical spatial coordinates for inputs; extreme learning machine, which is unable to deal with incomplete data; and ordinary ESD based RVM regression model without entropy weighted distance. The experimental results show that the proposed WESD-RVM outperforms other methods with considerable predictive and generalizing ability.

Journal

International Journal of Machine Learning and CyberneticsSpringer Journals

Published: May 3, 2016

References

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