@inproceedings{53add92c0e3842efa4d104f8820f2ddd,
title = "Moving towards accurate monitoring and prediction of gold mine underground dam levels",
abstract = "In this paper a comparison between an ensembles (multi-classifier) constructed of several machine learning methods (support vector machine, artificial neural network, naive Bayesian classifier, decision trees, radial basis function and k nearest neighbors) versus each single classifiers of these methods in term of gold mine underground dam levels prediction is presented. The ensembles as well as the single classifiers are used to classify, thus monitoring and predicting the underground water dam levels on a single-pump station deep gold in South Africa. In order to improve the classification accuracy an ensemble was constructed based on each single classifier performance, therefore, five ensembles were built and tested. In terms of misclassification error, the results show the ensemble to be more efficient for classification of underground water dam levels compared to each of the single classifiers.",
keywords = "Support vector machines, classification, de-watering system, ensembles, gold mines, naive Bayesian, neural networks, underground dam levels",
author = "Hasan, {Ali N.} and Bhekisipho Twala and Tshilidzi Marwala",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 International Joint Conference on Neural Networks, IJCNN 2014 ; Conference date: 06-07-2014 Through 11-07-2014",
year = "2014",
month = sep,
day = "3",
doi = "10.1109/IJCNN.2014.6889382",
language = "English",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2844--2849",
booktitle = "Proceedings of the International Joint Conference on Neural Networks",
address = "United States",
}
