Journal
NEUROCOMPUTING
Volume 74, Issue 16, Pages 2520-2525Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2010.12.034
Keywords
Fuzzy measure; Fuzzy integral; Upper integral; Multi-attribute classification; Possibility distribution; Extreme learning machine
Categories
Funding
- national natural science foundation of China [60903088, 60903089]
- natural science foundation of Hebei Province [F2010000323]
- Key Scientific Research Foundation of Education Department of Hebei Province [ZD2010139]
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The upper integral is a type of non-linear integral with respect to non-additive measures, which represents the maximum potential of efficiency for a group of features with interaction. The value of upper integrals can be evaluated through solving a linear programming problem. Considering the upper integral as a classifier, this paper first investigates its implementation and performance. Fusing multiple upper integral classifiers together by using a single layer neural network, this paper considers a upper integral network as a classification system. The learning mechanism of ELM is used to train this single layer neural network. A comparison of performance between a single upper integral classifier and the upper integral network is given on a number of benchmark databases. (C) 2011 Elsevier B.V. All rights reserved.
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