TY  - JOUR
TI  - Development of a supervised classification method to construct 2D mineral maps on backscattered electron images
AB  - The Mineral Liberation Analyzer (MLA) can be used to obtain mineral maps from backscattered electron(BSE) images of particles. This paper proposes an alternative methodology that includes random forest classification,a prospective machine learning algorithm, to develop mineral maps from BSE images. The results show that theoverall accuracy and kappa statistic of the proposed method are 97% and 0.94, respectively, proving that random forestclassification is accurate. The accuracy indicators also suggest that the proposed method may be applied to classifyminerals with similar appearances under BSE imaging. Meanwhile, random forest predicts fewer middling particles withbinary and ternary composition, but the MLA predicts more middling particles only with ternary composition. Thesediscrepancies may arise because the MLA, unlike random forest, may also measure the elemental compositions of mineralsurfaces below the polished section.
AU  - cavur, mahmut
AU  - Camalan, Mahmut
DO  - 10.3906/elk-1906-60
PY  - 2020
JO  - Turkish Journal of Electrical Engineering and Computer Sciences
VL  - 28
IS  - 2
SN  - 1300-0632
SP  - 1030
EP  - 1043
DB  - TRDizin
UR  - http://search/yayin/detay/335120
ER  -