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Neural network ensembles based approach for mineral prospectivity prediction

Iyer, V., Fung, C.C., Brown, W. and Wong, K.W. (2005) Neural network ensembles based approach for mineral prospectivity prediction. In: IEEE Region 10 Annual International Conference, Proceedings/TENCON, 21-24 Nov. 2005, Melbourne, Vic. pp. 1-5.

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    Link to Published Version: http://dx.doi.org/10.1109/TENCON.2005.300842
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    Abstract

    In mining industry, accurate identification of new geographic locations that are favourable for mineral exploration is very important. However, definitive prediction of such locations is not an easy task. In recent years, the use of neural networks ensemble approach to the classification problem has gained much attention. This paper discusses the results obtained from using different neural network (NN) ensemble techniques for the mineral prospectivtity prediction problem. The proposed model uses the Geographic Information Systems (GIS) data of the location. The method is tested on the GIS data for the Kalgoorlie region of Western Australia. The results obtained are compared to some of the commonly known techniques: the majority combination rule, averaging technique, weighted averaging method tuned by Genetic Algorithm (GA) and a newly proposed rule based method. The results obtained using the different techniques are discussed.

    Publication Type: Conference Paper
    Murdoch Affiliation: School of Information Technology
    Publisher: IEEE
    Copyright: (c) 2005 IEEE.
    Notes: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    URI: http://researchrepository.murdoch.edu.au/id/eprint/608
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