Neural Network Ensembles for Classification Problems Using Multiobjective Genetic Algorithms

Authors:
David Lahoz;Pedro Mateo
Affiliations:
Dept. Métodos Estadísticos, Univ. Zaragoza, Spain;Dept. Métodos Estadísticos, Univ. Zaragoza, Spain
Venue:
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Year:
2008

Citing 9
Cited 0

Genetic algorithms + data structures = evolution programs (3rd ed.)

Genetic algorithms + data structures = evolution programs (3rd ed.)
Bagging predictors

Machine Learning
An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization

Machine Learning
MultiBoosting: A Technique for Combining Boosting and Wagging

Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms

Multi-Objective Optimization Using Evolutionary Algorithms
Combining Classifiers with Meta Decision Trees

Machine Learning
Ensemble Methods in Machine Learning

MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Is Combining Classifiers with Stacking Better than Selecting the Best One?

Machine Learning
Cooperative coevolution of artificial neural network ensembles for pattern classification

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work a Multiobjective Genetic Algorithm is developed in order to obtain an appropriate ensemble of neural networks. The algorithm does not use any back-propagation method. Furthermore, it considers directly the classification error instead of the mean square error. To obtain the multiobjective environment, the training pattern set is divided into subsets such that each one has its own error function and then, all the error functions are considered simultaneously. The proposed algorithm is found to be competitive with other current methods in the literature.