Improving adaptive boosting with a relaxed equation to update the sampling distribution

Authors:
Joaquín Torres-Sospedra;Carlos Hernández-Espinosa;Mercedes Fernández-Redondo
Affiliations:
Departamento de Ingenieria y Ciencia de los Computadores, Universitat Jaume I, Castellon, Spain;Departamento de Ingenieria y Ciencia de los Computadores, Universitat Jaume I, Castellon, Spain;Departamento de Ingenieria y Ciencia de los Computadores, Universitat Jaume I, Castellon, Spain
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

Citing 2
Cited 0

Using Diversity with Three Variants of Boosting: Aggressive, Conservative, and Inverse

MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Boosting with averaged weight vectors

MCS'03 Proceedings of the 4th international conference on Multiple classifier systems

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Abstract

Adaptive Boosting (Adaboost) is one of the most known methods to build an ensemble of neural networks. In this paper we briefly analyze and mix two of the most important variants of Adaboost, Averaged Boosting and Conservative Boosting, in order to build a robuster ensemble of neural networks. The mixed method called Averaged Conservative Boosting (ACB) applies the conservative equation used in Conserboost along with the averaged procedure used in Aveboost in order to update the sampling distribution. We have tested the methods with seven databases from the UCI repository. The results show that Averaged Conservative Boosting is the best performing method.