Adaptive Classifiers-Ensemble System for Tracking Concept Drift

Adapting to various types of concept drift is important for dealing with real-world online learning problems. To achieve this, we previously reported an online learning system that uses an ensemble of classifiers, the adaptive classifiers-ensemble (ACE) system. ACE consists of one online classifier, many batch classifiers, and a drift detection mechanism. To improve the performance of ACE, we have improved the weighting method, which combines the outputs of classifiers, and have added a new classifier pruning method. Experimental results showed that the enhanced ACE performed well for a synthetic dataset that contained both sudden and gradual changes and recurring concepts.