An Integrated Framework for Data-Driven Mineral Prospectivity Mapping Using Bagging-Based Positive-Unlabeled Learning and Bayesian Cost-Sensitive Logistic Regression-Reference-Cited by-同舟云学术

An Integrated Framework for Data-Driven Mineral Prospectivity Mapping Using Bagging-Based Positive-Unlabeled Learning and Bayesian Cost-Sensitive Logistic Regression

Published:2022-09-21 Issue:6 Volume:31 Page:3041-3060
ISSN:1520-7439
Container-title:Natural Resources Research
language:en
Short-container-title:Nat Resour Res

Author:

Zhang Zhiqiang^ORCID,Wang Gongwen,Carranza Emmanuel John M.,Fan Junjie,Liu Xinxing,Zhang Xiang,Dong Yulong,Chang XiaoPeng,Sha Deming

Funder

Projects of CGS

2021 Natural Resource Research Student Awards, IAMG Student Awards

National Key Research and Development Programs of China

Publisher

Springer Science and Business Media LLC

Subject

General Environmental Science

Link

https://link.springer.com/content/pdf/10.1007/s11053-022-10120-0.pdf

Reference70 articles.

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3. Agterberg, F. P. (1989). Computer programs for mineral exploration. Science, 245, 7681.

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5. Alarifi, S. S., Kellogg, J. N., & Ibrahim, E. (2021). Geophysical Study of Gold Mineralized Zones in the Carolina Terrane of South Carolina: Economic Geology., 116, 1309–1327.

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