@inproceedings{d6d85804921c423b8c322c0239c01a14,
title = "Uncertainty in mineral prospectivity prediction",
abstract = "This paper presents an approach to the prediction of mineral prospectivity that provides an assessment of uncertainty. Two feedforward backpropagation neural networks are used for the prediction. One network is used to predict degrees of favourability for deposit and another one is used to predict degrees of likelihood for barren, which is opposite to deposit. These two types of values are represented in the form of truth-membership and false-membership, respectively. Uncertainties of type error in the prediction of these two memberships are estimated using multidimensional interpolation. These two memberships arid their uncertainties are combined to predict mineral deposit locations. The degree of uncertainty of type vagueness for each cell location is estimated and represented in the form of indeterminacy-membership value. The three memberships are then constituted into an interval neutrosophic set. Our approach improves classification performance compared to an existing technique applied only to the truth-membership value.",
author = "Pawalai Kraipeerapun and Fung, {Chun Che} and Warick Brown and Wong, {Kok Wai} and Tam{\'a}s Gedeon",
year = "2006",
doi = "10.1007/11893257_93",
language = "English",
isbn = "3540464816",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "841--849",
booktitle = "Neural Information Processing - 13th International Conference, ICONIP 2006, Proceedings",
address = "Germany",
note = "13th International Conference on Neural Information Processing, ICONIP 2006 ; Conference date: 03-10-2006 Through 06-10-2006",
}