Original Contribution: Stacked generalization
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Neural Computation
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Journal of Intelligent and Robotic Systems
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Adaptive mixtures of local experts
Neural Computation
Switching between selection and fusion in combining classifiers: anexperiment
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A theoretical framework for multiple neural network systems
Neurocomputing
On the Integration of Neural Classifiers through Similarity Analysis of Higher Order Features
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Computational Statistics & Data Analysis
Data dependence in combining classifiers
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
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MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
A new class of hybrid models for time series forecasting
Expert Systems with Applications: An International Journal
Combining neural networks for gait classification
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Applied Computational Intelligence and Soft Computing
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A limiting factor in research on combining classifiers is a lack of awareness of the full range of available modular structures. One reason for this is that there is as yet little agreement on a means of describing and classifying types of multiple classifier system. In this paper, a categorisation scheme for the identification and description of types of multinet systems is proposed in which systems are described as (a) involving competitive or cooperative combination mechanisms; (b) combining either ensemble, modular, or hybrid components; (c) relying on either bottom-up, or top-down combination, and (d) when bottom up as using either static or fixed combination methods. It is claimed that the categorisation provides an early, but necessary, step in the process of mapping the space of multinet systems: permitting the comparison of different types of system, and facilitating their design and description. On the basis of this scheme, one ensemble and two modular multinet system designs are implemented, and applied to an engine fault diagnosis problem. The best generalisation performance was achieved from the ensemble system.