Machine Learning - Special issue on learning with probabilistic representations
An efficient MDL-based construction of RBF networks
Neural Networks
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
A global learing algorithm for a RBF network
Neural Networks
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
The constraint based decomposition (CBD) training architecture
Neural Networks
Using Correspondence Analysis to Combine Classifiers
Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
The Coevolution of Antibodies for Concept Learning
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Parameter Control within a Co-operative Co-evolutionary Genetic Algorithm
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
An introduction to variable and feature selection
The Journal of Machine Learning Research
Redundant feature elimination for multi-class problems
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multiclass Boosting for Weak Classifiers
The Journal of Machine Learning Research
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Improving Multiclass Pattern Recognition by the Combination of Two Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hybrid approach for feature subset selection using neural networks and ant colony optimization
Expert Systems with Applications: An International Journal
A Real generalization of discrete AdaBoost
Artificial Intelligence
Immune network based ensembles
Neurocomputing
Improving the effectiveness of RBF classifier based on a hybrid cost function
Neural Computing and Applications
Expert Systems with Applications: An International Journal
Improving multiclass pattern recognition with a co-evolutionary RBFNN
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Discriminative metric design for robust pattern recognition
IEEE Transactions on Signal Processing
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
Inducing oblique decision trees with evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Cooperative coevolution of artificial neural network ensembles for pattern classification
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary learning of nearest-neighbor MLP
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Robotics and Computer-Integrated Manufacturing
| Hi-index | 12.06 |
There are irrelevant features that are redundant or significantly degrade the learning accuracy in the real-world complex classification tasks. This paper presents a new hybrid learning algorithm based on a cooperative coevolutionary algorithm (Co-CEA) with dual populations for designing the radial basis function neural network (RBFNN) models with an explicit feature selection. This approach attempts to complete both the RBFNN construction and the feature selection simultaneously. The proposed algorithm utilizes the Co-CEA's divide-and-cooperative mechanism, which utilizes the evolutionary algorithms executing in parallel to coevolve subpopulations, corresponding to the hidden layer structure and the dominate features respectively. The algorithm adopts the binary encoding to represent the feature subset and the matrix-form mixed encoding to represent the RBFNN hidden layer structure, and a complete solution is formed via collaborations among the two subpopulations. Experimental results illustrate that the proposed algorithm outperforms other algorithms in references in terms of the classification accuracy, and it is able to obtain both prominent features and good RBFNN structure with higher prediction capability.