ADABOOK & MULTIBOOK: Adaptive Boosting with Chance Correction

TL;DR

This paper introduces ADABOOK and MULTIBOOK, adaptive boosting algorithms that optimize chance-corrected measures, improving performance over traditional AdaBoost and MultiBoost, especially in multiclass scenarios.

Contribution

It proposes chance-corrected boosting algorithms, ADABOOK and MULTIBOOK, addressing early surrender issues and enhancing boosting effectiveness with empirical validation.

Findings

Chance-corrected measures improve boosting performance.

ADABOOK and MULTIBOOK outperform standard AdaBoost and MultiBoost.

Chance correction mitigates early surrender in multiclass boosting.

Abstract

There has been considerable interest in boosting and bagging, including the combination of the adaptive techniques of AdaBoost with the random selection with replacement techniques of Bagging. At the same time there has been a revisiting of the way we evaluate, with chance-corrected measures like Kappa, Informedness, Correlation or ROC AUC being advocated. This leads to the question of whether learning algorithms can do better by optimizing an appropriate chance corrected measure. Indeed, it is possible for a weak learner to optimize Accuracy to the detriment of the more reaslistic chance-corrected measures, and when this happens the booster can give up too early. This phenomenon is known to occur with conventional Accuracy-based AdaBoost, and the MultiBoost algorithm has been developed to overcome such problems using restart techniques based on bagging. This paper thus complements the theoretical work showing the necessity of using chance-corrected measures for evaluation, with empirical work showing how use of a chance-corrected measure can improve boosting. We show that the early surrender problem occurs in MultiBoost too, in multiclass situations, so that chance-corrected AdaBook and Multibook can beat standard Multiboost or AdaBoost, and we further identify which chance-corrected measures to use when.