The paper presents an adaptive GP boosting ensemble method forthe classification of distributed homogeneous streaming data that comes from multiple locations. The approach is able to handle concept drift via change detection by employing a change detection strategy, based on self-similarity of the ensemble behavior, and measured by its fractal dimension. It is efficient since each nodeof the network works with its local streaming data, and communicate only the local model computed with the otherpeer-nodes. Furthermore, once the ensemble has been built, it isused to predict the class membership of new streams of data until concept drift is detected. Only in such a case the algorithm is executed to generate a new set of classifiers to update the current ensemble. Experimental results on a synthetic and reallife data set showed the validity of the approach in maintaining an accurate and up-to-date GP ensemble.
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