Cooperative coevolutionary ensemble learning

Authors:
Daniel Kanevskiy;Konstantin Vorontsov
Affiliations:
Computing Center of the Russian Academy of Sciences, Moscow, Russia;Computing Center of the Russian Academy of Sciences, Moscow, Russia
Venue:
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Year:
2007

Citing 11
Cited 0

The Random Subspace Method for Constructing Decision Forests

IEEE Transactions on Pattern Analysis and Machine Intelligence
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
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants

Machine Learning
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy

Machine Learning
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Exploiting Reliability for Dynamic Selection of Classifiers by Means of Genetic Algorithms

ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Combining Pattern Classifiers: Methods and Algorithms

Combining Pattern Classifiers: Methods and Algorithms
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents

Evolutionary Computation
Genetic algorithm based selective neural network ensemble

IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Designing classifier fusion systems by genetic algorithms

IEEE Transactions on Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

A new optimization technique is proposed for classifier fusion -- Cooperative Coevolutionary Ensemble Learning (CCEL). It is based on a specific multipopulational evolutionary algorithm -- cooperative coevolution. It can be used as a wrapper over any kind of weak algorithms, learning procedures and fusion functions, for both classification and regression tasks. Experiments on the real-world problems from the UCI repository show that CCEL has a fairly high generalization performance and generates ensembles of much smaller size than boosting, bagging and random subspace method.