An Adaptive Distributed Ensemble Approach to Mine Concept-Drifting Data Streams

An adaptive boosting ensemble algorithm for classifying homogeneous distributed data streams is presented. The method builds an ensemble of classifiers by using Genetic Programming (GP) to inductively generate decision trees, each trained on different parts of the distributed training set. The approach adopts a co-evolutionary platform to support a cooperative model of GP. A change detection strategy, based on self-similarity of the ensemble behavior, and measured by its fractal dimension, permits to capture time- evolving trends and patterns in the stream, and to reveal changes in evolving data streams. The approach tracks online ensemble accuracy deviation over time and decides to recompute the ensemble if the deviation has exceeded a pre- specified threshold. This allows the maintenance of an accurate and up-to-date ensemble of classifiers for continuous flows of data with concept drifts. Experimental results on a real life data set show the validity of the approach.