Efficiently Maintaining the Performance of an Ensemble Classifier in Streaming Data

Abstract

In data stream environments, classifiers are generally refined based on regular time interval or fixed number of streaming data. Also, the correct labels of all unlabeled streaming data are typically used in the refine process. Such an approach is not feasible in many real world applications where data labeled by human experts should be used to improve classifiers. In this paper, we select data for refining a classifier from streaming data in an online process. Our selection methodology uses training data, and is applied to build an ensemble of classifiers over streaming data. We compared the results of our ensemble approach and of a conventional ensemble approach where new classifiers for an ensemble are periodically generated. In experiments with ten benchmark data sets including three real streaming data sets, our ensemble approach generated an average of 2.4% classifiers using an average of 10.0% labeled data for the conventional ensemble approach, and produced comparable classification accuracy.