Soft combination of neural classifiers: a comparative study
Pattern Recognition Letters
Fusion of neural networks with fuzzy logic and genetic algorithm
Integrated Computer-Aided Engineering
Dynamic image sequence analysis using fuzzy measures
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Comparative study of fuzzy methods for response integration in ensemble neural networks
International Journal of Advanced Intelligence Paradigms
Fuzzy integral to speed up support vector machines training for pattern classification
International Journal of Knowledge-based and Intelligent Engineering Systems
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Combining multiple neural networks has been used to improve the decision accuracy in many application fields including pattern recognition and classification. In this paper, we investigate the potential of this approach for land cover change detection. In a first step, we perform many experiments in order to find the optimal individual networks in terms of architecture and training rule. In the second step, different neural network change detectors are combined using amethod based on the notion of fuzzy integral. This method combines objective evidences in the form of network outputs, with subjective measures of their performances. Various forms of the fuzzy integral, which are, namely, Choquet integral, Sugeno integral, and two extensions of Sugeno integral with ordered weighted averaging operators, are implemented. Experimental analysis using error matrices and Kappa analysis showed that the fuzzy integral outperforms individual networks and constitutes an appropriate strategy to increase the accuracy of change detection.