Neural networks and the bias/variance dilemma
Neural Computation
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Unifeid Bias-Variance Decomposition and its Applications
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Pareto Optimality in Coevolutionary Learning
ECAL '01 Proceedings of the 6th European Conference on Advances in Artificial Life
ICDAR '95 Proceedings of the Third International Conference on Document Analysis and Recognition (Volume 1) - Volume 1
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Improving multiclass pattern recognition with a co-evolutionary RBFNN
Pattern Recognition Letters
A scalable framework for cluster ensembles
Pattern Recognition
CIXL2: a crossover operator for evolutionary algorithms based on population features
Journal of Artificial Intelligence Research
Switching class labels to generate classification ensembles
Pattern Recognition
Ensemble techniques for parallel genetic programming based classifiers
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Evolving artificial neural network ensembles
IEEE Computational Intelligence Magazine
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Training genetic programming on half a million patterns: an example from anomaly detection
IEEE Transactions on Evolutionary Computation
Cooperative coevolution of artificial neural network ensembles for pattern classification
IEEE Transactions on Evolutionary Computation
Making use of population information in evolutionary artificialneural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A constructive algorithm for training cooperative neural network ensembles
IEEE Transactions on Neural Networks
A remote sensing image classification method based on extreme learning machine ensemble
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
| Hi-index | 0.01 |
Ensemble approaches to classification have attracted a great deal of interest recently. This paper presents a novel method for designing the neural network ensemble using coevolutionary algorithm. The bootstrap resampling procedure is employed to obtain different training subsets that are used to estimate different component networks of the ensemble. Then the cooperative coevolutionary algorithm is developed to optimize the ensemble model via the divide-and-cooperative mechanism. All component networks are coevolved in parallel in the scheme of interacting co-adapted subpopulations. The fitness of an individual from a particular subpopulation is assessed by associating it with the representatives from other subpopulations. In order to promote the cooperation of all component networks, the proposed method considers both the accuracy and the diversity among the component networks that are evaluated using the multi-objective Pareto optimality measure. A hybrid output-combination method is designed to determine the final ensemble output. Experimental results illustrate that the proposed method is able to obtain neural network ensemble models with better classification accuracy in comparison with currently popular ensemble algorithms.