Dataset for: Machine learning application to automatically classify heavy minerals in river sand by using SEM/EDS data

Published: 03-07-2019| Version 1 | DOI: 10.17632/t6t82b2h7h.1
huizhen hao,
Ruohua Guo,
Qing Gu,
Xiumian Hu


Among them, three tables include the EDS data of analyzed elements including 90-second elemental data of 2255 grains, 40-second elemental data of 492 grains and 6- second elemental data of 320 grains, respectively. The tables 4-7 include the confusion matrixes based on datasets of different analyzing times and different decision attributes.