Weighted Aging Classifier Ensemble for the Incremental Drifted Data Streams
Evolving systems are recently focus of intense research because for most of the real problems we can observe that the parameters of the decision tasks should adapt to new conditions. In classification such a problem is usually called concept drift. The paper deals with the data stream classification where we assume that the concept drift is sudden but its rapidity is limited. To deal with this problem we propose a new algorithm called Weighted Aging Ensemble (WAE), which is able to adapt to changes of classification model parameters. The method is inspired by well-known algorithm Accuracy Weighted Ensemble (AWE) which allows to change the line-up of a classifier ensemble, but the proposed method incudes two important modifications: (i) classifier weights depend on the individual classifier accuracies and time they have been spending in the ensemble, (ii) individual classifier are chosen to the ensemble on the basis on the non-pairwise diversity measure. The proposed method was evaluated on the basis of computer experiments which were carried out on SEA dataset. The obtained results encourage us to continue the work on the proposed concept.
Keywordsmachine learning classifier ensemble data stream concept drift incremental learning forgetting
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