Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/19041
Title: Extreme learning machine for determining signed efficiency measure from data
Authors: Li, Y
Ng, PHF
Shiu, SCK 
Keywords: Extreme learning machine
Fuzzy integral
Fuzzy measure
Machine learning
Issue Date: 2013
Publisher: World Scientific Publ Co Pte Ltd
Source: International journal of uncertainty, fuzziness and knowlege-based systems, 2013, v. 21, no. suppl.2, p. 131-142 How to cite?
Journal: International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems 
Abstract: The techniques of fuzzy measure and fuzzy integral have been successfully applied in various real-world applications. The determination of fuzzy measures is the most difficult part in problem solving. Signed efficiency measure, which is a special kind of fuzzy measure with the best representation ability but the highest complexity, is even harder to determine. Some methodologies have been developed for solving this problem such as artificial neural networks (ANNs) and genetic algorithms (GAs). However, none of the existing methods can outperform the others with unique advantages. Thus, there is a strong need to develop a new technique for learning distinct signed efficiency measures from data. Extreme learning machine (ELM) is a new learning paradigm for training single hidden layer feed-forward networks (SLFNs) with randomly chosen input weights and analytically determined output weights. In this paper, we propose an ELM based algorithm for signed efficiency measure determination. Experimental comparisons demonstrate the effectiveness of the proposed method in both time and accuracy.
URI: http://hdl.handle.net/10397/19041
ISSN: 0218-4885
DOI: 10.1142/S0218488513400217
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