Robust classification systems for imprecise environments
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A Memetic Pareto Evolutionary Approach to Artificial Neural Networks
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Introducing Multi-objective Optimization in Cooperative Coevolution of Neural Networks
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Speeding up backpropagation using multiobjective evolutionary algorithms
Neural Computation
Data mining in metric space: an empirical analysis of supervised learning performance criteria
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Learning to Use a Learned Model: A Two-Stage Approach to Classification
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Neural network classification of homomorphic segmented heart sounds
Applied Soft Computing
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A multi-objective approach to RBF network learning
Neurocomputing
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
A multiobjective simultaneous learning framework for clustering and classification
IEEE Transactions on Neural Networks
Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
Simultaneous optimization of weights and structure of an RBF neural network
EA'05 Proceedings of the 7th international conference on Artificial Evolution
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Pareto-Based Multiobjective Machine Learning: An Overview and Case Studies
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
An evolutionary artificial neural networks approach for breast cancer diagnosis
Artificial Intelligence in Medicine
IEEE Transactions on Neural Networks
Pareto evolutionary neural networks
IEEE Transactions on Neural Networks
International Journal of Systems Biology and Biomedical Technologies
Hi-index | 0.00 |
This paper presents a new multi-objective evolutionary hybrid algorithm for the design of Radial Basis Function Networks (RBFNs) for classification problems. The algorithm, MEPDEN, Memetic Elitist Pareto evolutionary approach based on the Non-dominated Sorting Differential Evolution (NSDE) multi-objective evolutionary algorithm which has been adapted to design RBFNs, where the NSDE algorithm is augmented with a local search that uses the Back-propagation algorithm. The MEPDEN is tested on two-class and multiclass pattern classification problems. The results obtained in terms of Mean Square Error (MSE), number of hidden nodes, accuracy (ACC), sensitivity (SEN), specificity (SPE) and Area Under the receiver operating characteristics Curve (AUC), show that the proposed approach is able to produce higher prediction accuracies with much simpler network structures. The accuracy and complexity of the network obtained by the proposed algorithm are compared with Memetic Eilitist Pareto Non-dominated Sorting Genetic Algorithm based RBFN (MEPGAN) through statistical tests. This study showed that MEPDEN obtains RBFNs with an appropriate balance between accuracy and simplicity, outperforming the other method considered.