Conference proceeding
Improving Single Classifiers Prediction Accuracy for Underground Water Pump Station in a Gold Mine Using Ensemble Techniques
IEEE EUROCON 2015 - INTERNATIONAL CONFERENCE ON COMPUTER AS A TOOL (EUROCON), pp.19-25
01 Jan 2015
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
in this paper six single classifiers (support vector machine, artificial neural network, naive 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.
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Details
- Title
- Improving Single Classifiers Prediction Accuracy for Underground Water Pump Station in a Gold Mine Using Ensemble Techniques
- Creators
- Ali N. Hasan - University of JohannesburgBhekisipho Twala - University of Johannesburg
- Contributors
- J Haase (Editor)A Kakarountas (Editor)M Grana (Editor)J Fraile-Ardanuy (Editor)C J Debono (Editor)H Quintian (Editor)E Corchado (Editor)
- Publication Details
- IEEE EUROCON 2015 - INTERNATIONAL CONFERENCE ON COMPUTER AS A TOOL (EUROCON), pp.19-25
- Publisher
- IEEE
- Number of pages
- 7
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Engineering Leadership and Society/Engineering Technology
- Identifiers
- 991022004764004721
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