Abstract Machine learning algorithms (MLAs) such us artificial neural networks (ANNs),
regression trees (RTs), random forest (RF) and support vector machines (SVMs) are …

Abstract Machine learning algorithms have been widely applied in mineral prospectivity
mapping (MPM). In this study, we implemented ensemble learning of extreme gradient …

Various ensemble machine learning techniques have been widely studied and implemented
to construct the predictive models in different sciences, including bagging, boosting, and …

Abstract Machine learning methods that have been used in data-driven predictive modeling
of mineral prospectivity (eg, artificial neural networks) invariably require large number of …

Predictive modelling of mineral prospectivity, a critical, but challenging procedure for
delineation of undiscovered prospective targets in mineral exploration, has been spurred by …

Identification of anomalies related to mineralization and integration of multi-source
geoscience data are essential for mapping mineral prospectivity. In this study, we applied …

The excellent performance of convolutional neural network (CNN) and its variants in image
classification makes it a potential perfect candidate for dealing with multi-geoinformation …

Mineral exploration activities require robust predictive models that result in accurate
mapping of the probability that mineral deposits can be found at a certain location. Random …

Abstract The Random Forests (RF) algorithm is a machine learning method that has recently
been demonstrated as a viable technique for data-driven predictive modeling of mineral …

In this research, we conduct a case study of mapping polymetallic prospectivity using an
extreme learning machine (ELM) regression. A Quad-Core CPU 1.8 GHz laptop computer …