Connectionist learning procedures
Artificial Intelligence
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
C4.5: programs for machine learning
C4.5: programs for machine learning
Automated knowledge acquisition
Automated knowledge acquisition
Structural learning with forgetting
Neural Networks
Machine Learning
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Ensemble of Evolving Neural Networks in Classification
Neural Processing Letters
Ensemble of Genetic Programming Models for Designing Reactive Power Controllers
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
An ensemble method using hybrid real-coded genetic algorithm with pruning (HRGA/PR)
PDCS '07 Proceedings of the 19th IASTED International Conference on Parallel and Distributed Computing and Systems
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To obtain a classification model with high generalization ability, this paper proposes a novel ensemble method that implements a hybrid real-coded genetic algorithm with pruning (HRGA/P). A crucial idea here is to combine ensemble learning and HRGA/P with parallel computational ability and high generalization ability. Accordingly, the resulting classification model is expected to have high generalization ability. Applications of the proposed method to a wine classification problem well demonstrate its effectiveness. The characteristics of generalization ability of an interpolated model from two classification models are also investigated.