EFuNN ensembles construction using a clustering method and a coevolutionary multi-objective genetic algorithm

Authors:
Fernanda L. Minku;Teresa B. Ludermir
Affiliations:
Federal University of Pernambuco, Informatics Center, Brazil;Federal University of Pernambuco, Informatics Center, Brazil
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Year:
2006

Citing 6
Cited 2

Data mining: practical machine learning tools and techniques with Java implementations

Data mining: practical machine learning tools and techniques with Java implementations
Time Series Analysis, Forecasting and Control

Time Series Analysis, Forecasting and Control
Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines

Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines
EFuNN Ensembles Construction Using CONE with Multi-objective GA

SBRN '06 Proceedings of the Ninth Brazilian Symposium on Neural Networks
Evolving fuzzy neural networks for supervised/unsupervised onlineknowledge-based learning

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Clustering and co-evolution to construct neural network ensembles: An experimental study

Neural Networks
A decade of Kasabov's evolving connectionist systems: a review

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents the experiments which where made with the Clustering and Coevolution to Construct Neural Network Ensemble (CONE) approach on two classification problems and two time series prediction problems. This approach was used to create a particular type of Evolving Fuzzy Neural Network (EFuNN) ensemble and optimize its parameters using a Coevolutionary Multi-objective Genetic Algorithm. The results of the experiments reinforce some previous results which have shown that the approach is able to generate EFuNN ensembles with accuracy either better or equal to the accuracy of single EFuNNs generated without using coevolution. Besides, the execution time of CONE to generate EFuNN ensembles is lower than the execution time to produce single EFuNNs without coevolution.