Tracking Concept Change with Incremental Boosting by Minimization of the Evolving Exponential Loss

Abstract

Methods involving ensembles of classifiers, such as bagging and boosting, are popular due to the strong theoretical guarantees for their performance and their superior results. Ensemble methods are typically designed by assuming the training data set is static and completely available at training time. As such, they are not suitable for online and incremental learning. In this paper we propose IBoost , an extension of AdaBoost for incremental learning via optimization of an exponential cost function which changes over time as the training data changes. The resulting algorithm is flexible and allows a user to customize it based on the computational constraints of the particular application. The new algorithm was evaluated on stream learning in presence of concept change. Experimental results showed that IBoost achieves better performance than the original AdaBoost trained from scratch each time the data set changes, and that it also outperforms previously proposed Online Coordinate Boost , Online Boost and its non-stationary modifications, Fast and Light Boosting , ADWIN Online Bagging and DWM algorithms.