Multi-class Boosting with Class Hierarchies

Authors:
Goo Jun;Joydeep Ghosh
Affiliations:
Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, USA TX-78712;Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, USA TX-78712
Venue:
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Year:
2009

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Abstract

We propose AdaBoost.BHC, a novel multi-class boosting algorithm. AdaBoost.BHC solves a C class problem by using C *** 1 binary classifiers defined by a hierarchy that is learnt on the classes based on their closeness to one another. It then applies AdaBoost to each binary classifier. The proposed algorithm is empirically evaluated with other multi-class AdaBoost algorithms using a variety of datasets. The results show that AdaBoost.BHC is consistently among the top performers, thereby providing a very reliable platform. In particular, it requires significantly less computation than AdaBoost.MH, while exhibiting better or comparable generalization power.