Precision-based Boosting

AdaBoost is a highly popular ensemble classification method for which many variants have been published. This paper proposes a generic refinement of all of these AdaBoost variants. Instead of assigning weights based on the total error of the base classifiers (as in AdaBoost), our method uses classspecific error rates. On instance x it assigns a higher weight to a classifier predicting label y on x, if that classifier is less likely to make a mistake when it predicts class y. Like Ada- Boost, our method is guaranteed to boost weak learners into strong learners. An empirical study on AdaBoost and one of its multi-class versions, SAMME, demonstrates the superiority of our method on datasets with more than 1, 000 instances as well as on datasets with more than three classes.

Authors

• Mohammad Hossein Nikravan
• Marjan Movahedan
• Sandra Zilles University of Regina, Canada

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