earticle

영어

We propose ensemble-based modeling for classifying streaming data with concept drift. The concept drift is a phenomenon in which the distribution of streaming data changes. In this paper, the types of the concept drift are categorized into the change of data distribution and the change of class distribution. The proposed ensemble modeling generates a meta-ensemble which consists of ensembles of classifiers. Whenever a change of class distribution occurs in streaming data, our modeling builds a new classifier of an existing ensemble and whenever a change of data distribution occurs, it builds a new ensemble which consists of an only one classifier. In our approach, new classifiers of a meta-ensemble on streaming data will be generated dynamically according to the estimated distribution of streaming data. We compared the results of our approach and of the chunk-based ensemble approach, which builds new classifiers of an ensemble periodically. In experiments with 13 benchmark data sets, our approach produced an average of 21.95% higher classification accuracy generating an average of 61.7% fewer new classifiers of an ensemble than the chunk-based ensemble method using partially labeled samples. We also examine that the time points when our approach builds new classifiers are appropriate for maintaining performance of an ensemble.