An Online Adaptive Classifier Ensemble For Mining Non-Stationary Data Streams

Many real-world situations constantly generate concept-drifting data streams at high speed. These situations demand adaptive algorithms able to learn online in accordance with the most recent target function (concept). This paper presents Online Adaptive Classifier Ensemble, a new ensemble algorithm able to learn from concept-drifting data streams. The proposed algorithm uses a change detection mechanism in each base classifier in order to handle possible changes in the underlying target function. Each base classifier in the ensemble can alternate between three different stages during the learning process: stable, warning and drift. In a stable stage, the underlying target function is supposed to remain constant, and the corresponding base classifier is updated with each incoming training instance. In a warning stage, a possible change in the target function can be starting to occur, and an alternative base classifier is created and trained together with the other base classifiers. The alternative classifier is added to the ensemble if the drift stage is reached. The new algorithm is compared with various state-of-the-art ensemble algorithms for online learning. Empirical studies show that this proposal is an effective alternative for learning from non-stationary data streams.