Machine Learning
Optimal linear combinations of neural networks
Neural Networks
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ensemble learning via negative correlation
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
PLEASE: A Prototype Learning System Using Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Evolutionary Design of Nearest Prototype Classifiers
Journal of Heuristics
A Comparison of Decision Tree Ensemble Creation Techniques
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facing classification problems with Particle Swarm Optimization
Applied Soft Computing
Ensemble learning for free with evolutionary algorithms?
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Genetic algorithm based selective neural network ensemble
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Multiple-prototype classifier design
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Designing classifier fusion systems by genetic algorithms
IEEE Transactions on Evolutionary Computation
Making use of population information in evolutionary artificialneural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Application of majority voting to pattern recognition: an analysis of its behavior and performance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A novel ensemble algorithm for biomedical classification based on Ant Colony Optimization
Applied Soft Computing
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This paper proposes and evaluates a Particle Swarm Optimization (PSO) based ensemble classifier. The members of the ensemble are Nearest Prototype Classifiers generated sequentially using PSO and combined by a majority voting mechanism. Two necessary requirements for good performance of an ensemble are accuracy and diversity of error. Accuracy is achieved by PSO minimizing a fitness function representing the error rate as the members are created. The diversity of error is promoted by using a different initialization of PSO each time to create a new member and by adopting decorrelated training where a penalty term is added to the fitness function to penalize particles that make the same errors as previously generated classifiers. Simulation experiments on different classification problems show that the ensemble has better performance than a single classifier and are effective in generating diverse ensemble members.