Self-poised ensemble learning

Authors:
Ricardo Ñanculef;Carlos Valle;Héctor Allende;Claudio Moraga
Affiliations:
Departamento de Informática, Universidad Federico Santa María, Valparaíso, Chile;Departamento de Informática, Universidad Federico Santa María, Valparaíso, Chile;Departamento de Informática, Universidad Federico Santa María, Valparaíso, Chile;Dortmund Universitaet, Dortmund, Deutschland
Venue:
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Year:
2005

Citing 10
Cited 1

The Strength of Weak Learnability

Machine Learning
Bagging predictors

Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Ensemble learning via negative correlation

Neural Networks
Machine Learning

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

Machine Learning
Improving Regressors using Boosting Techniques

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
An introduction to boosting and leveraging

Advanced lectures on machine learning
Intelligent data analysis

Intelligent data analysis
An empirical evaluation of bagging and boosting

AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence

Moderated innovations in self-poised ensemble learning

CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a new approach to train ensembles of learning machines in a regression context. At each iteration a new learner is added to compensate the error made by the previous learner in the prediction of its training patterns. The algorithm operates directly over values to be predicted by the next machine to retain the ensemble in the target hypothesis and to ensure diversity. We expose a theoretical explanation which clarifies what the method is doing algorithmically and allows to show its stochastic convergence. Finally, experimental results are presented to compare the performance of this algorithm with boosting and bagging in two well-known data sets.