Abstract
In this paper six single classifiers (support
vector machine, artificial neural network, naïve
Bayesian classifier, decision trees, radial basis function
and k nearest neighbors) were utilized to predict water
dam levels in a deep gold mine underground pump
station. Also, Bagging and Boosting ensemble
techniques were used to increase the prediction
accuracy of the single classifiers. In order to enhance
the prediction accuracy even more a mutual
information ensemble approach is introduced to
improve the single classifiers and the Bagging and
Boosting prediction results. This ensemble is used to
classify, thus monitoring and predicting the
underground water dam levels on a single-pump
station deep gold mine in South Africa, Mutual
information theory is used in order to determine the
classifiers optimum number to build the most accurate
ensemble. In terms of prediction accuracy, the results
show that the mutual information ensemble over
performed the other used ensembles and single
classifiers and is more efficient for classification of
underground water dam levels. However the ensemble
construction is more complicated than the Bagging and
Boosting techniques.