Geochemical Fingerprinting of Coltan Ores by Machine Learning on Uneven Datasets |
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| Authors: | Christian Savu-Krohn Gerd Rantitsch Peter Auer Frank Melcher Torsten Graupner |
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| Institution: | 1.Department of Applied Geosciences and Geophysics,Montanuniversit?t Leoben,Leoben,Austria;2.Chair for Information Technology, Montanuniversit?t Leoben,Leoben,Austria;3.Federal Institute for Geosciences and Natural Resources,Hannover,Germany |
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| Abstract: | Two modern machine learning techniques, Linear Programming Boosting (LPBoost) and Support Vector Machines (SVMs), are introduced
and applied to a geochemical dataset of niobium–tantalum (“coltan”) ores from Central Africa to demonstrate how such information
may be used to distinguish ore provenance, i.e., place of origin. The compositional data used include uni- and multivariate
outliers and elemental distributions are not described by parametric frequency distribution functions. The “soft margin” techniques
of LPBoost and SVMs can be applied to such data. Optimization of their learning parameters results in an average accuracy
of up to c. 92%, if spot measurements are assessed to estimate the provenance of ore samples originating from two geographically
defined source areas. A parameterized performance measure, together with common methods for its optimization, was evaluated
to account for the presence of uneven datasets. Optimization of the classification function threshold improves the performance,
as class importance is shifted towards one of those classes. For this dataset, the average performance of the SVMs is significantly
better compared to that of LPBoost. |
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