Boosting inspired process for improving AUC

Victor S. Sheng, Rahul Tada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

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.

Original languageEnglish
Title of host publicationMachine Learning and Data Mining in Pattern Recognition - 7th International Conference, MLDM 2011, Proceedings
Pages199-209
Number of pages11
DOIs
StatePublished - Sep 7 2011
Event7th International Conference on Machine Learning and Data Mining, MLDM 2011 - New York, NY, United States
Duration: Aug 30 2011Sep 3 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6871 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Machine Learning and Data Mining, MLDM 2011
CountryUnited States
CityNew York, NY
Period08/30/1109/3/11

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Keywords

  • AUC
  • AUCBoost
  • boosting
  • classification
  • data mining
  • decision tree
  • inductive learning
  • machine learning
  • naïve bayes

Cite this

Sheng, V. S., & Tada, R. (2011). Boosting inspired process for improving AUC. In Machine Learning and Data Mining in Pattern Recognition - 7th International Conference, MLDM 2011, Proceedings (pp. 199-209). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6871 LNAI). https://doi.org/10.1007/978-3-642-23199-5_15