Boosted Tree Ensembles for Solving Multiclass Problems

Authors:
Terry Windeatt;Gholamreza Ardeshir
Affiliations:
-;-
Venue:
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Year:
2002

Citing 12
Cited 2

Simplifying decision trees

International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
C4.5: programs for machine learning

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A Comparative Analysis of Methods for Pruning Decision Trees

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

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Using output codes to boost multiclass learning problems

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Pruning Adaptive Boosting

ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Reducing multiclass to binary: a unifying approach for margin classifiers

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Simplifying decision trees: A survey

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Solving multiclass learning problems via error-correcting output codes

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Consolidated trees: an analysis of structural convergence

Data Mining

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Abstract

In this paper we consider the combination of two ensemble techniques, both capable of producing diverse binary base classifiers. Adaboost, a version of Boosting is combined with Output Coding for solving multiclass problems. Decision trees are chosen as the base classifiers, and the issue of tree pruning is addressed. Pruning produces less complex trees and sometimes leads to better generalisation. Experimental results demonstrate that pruning makes little difference in this framework. However, on average over nine benchmark datasets better accuracy is achieved by incorporating unpruned trees.