Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
Fusion of n-Tuple Based Classifiers for High Performance Handwritten Character Recognition
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Combining Decision Trees and Neural Networks for Drug Discovery
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
Trainable Multiple Classifier Schemes for Handwritten Character Recognition
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Recognition of Cursive Roman Handwriting - Past, Present and Future
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
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We describe a multiple classifier system which incorporates an automatic self-configuration scheme based on genetic algorithms. Our main interest in this paper is focused on exploring the statistical properties of the resulting multi-expert configurations. To this end we initially test the proposed system on a series of tasks of increasing difficulty drawn from the domain of character recognition. We then proceed to investigate the performance of our system not only in comparison to that of its constituent classifiers, but also in comparison to an independent set of individually optimised classifiers. Our results illustrate that significant gains can be obtained by integrating a genetic algorithm based optimisation process into multi-classifier schemes both in the performance enhancement and in the reduction of its volatility, especially as the task domain becomes more complex.