Abstract
In this paper a comparison between two
single classifier methods (support vector machine,
artificial neural network) and two ensemble methods
(bagging, and boosting) is applied to a real-world
mining problem. The four methods are used to classify,
thus monitoring underground dam levels and
underground pumps energy consumption on a doublepump
station deep gold in South Africa. In terms of
misclassification error, the results show support vector
machines (SVM) to be more efficient for classification of
underground pumps energy consumption compared to
artificial neural network (ANN),...