Boosting inspired process for improving AUC

Authors:
Victor S. Sheng;Rahul Tada
Affiliations:
Department of Computer Science, University of Central Arkansas, Conway, Arkansas;Department of Computer Science, University of Central Arkansas, Conway, Arkansas
Venue:
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Year:
2011

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Abstract

Boosting is a general method of combining a set of classifiers in making final prediction. It is shown to be an effective approach to improve the predictive accuracy of a learning algorithm, but its impact on the ranking performance is unknown. This paper introduces the boosting algorithm AUCBoost, which is a generic algorithm to improve the ranking performance of learning algorithms. Unlike AdaBoost, AUCBoost uses the AUC, not the accuracy, of a classifier to calculate the weight of each training example for building next classifier. To simplify the computation of AUC of weighted instances in AUCBoost, we extend the standard formula for calculating AUC to be a weighted AUC formula (WAUC in short). This extension frees boosting from the resampling process and saves much computation time in the training process. Our experiment results show that the new boosting algorithm AUCBoost does improve ranking performance of AdaBoost when the base learning algorithm is the improved ranking favored decision tree C4.4 or naïve Bayes.