Ensemble Classifier for Mining Data Streams

The problem addressed in this paper concerns mining data streams with concept drift. The goal of the paper is to propose and validate a new approach to mining data streams with concept-drift using the ensemble classifier constructed from the one-class base classifiers. It is assumed that base classifiers of the proposed ensemble are induced from incoming chunks of the data stream. Each chunk consists of prototypes and can be updated using instance selection technique when a new data have arrived. When a new data chunk is formed, ensemble model is also updated on the basis of weights assigned to each one-class classifier. The proposed approach is validated experimentally.