Empirical Support for Concept Drifting Approaches: Results Based on New Performance Metrics
Division of COE, NSIT, Dwarka, New Delhi, 110078, India, Parneeta Sidhu, M. P. S. Bhatia · International Journal of Intelligent Systems and Applications · 2015
Various types of online learning algorithms have been developed so far to handle concept drift in data streams.We perform more detailed evaluation of these algorithms through new performance metrics -prequential accuracy, kappa statistic, CPU evaluation time, model cost, and memory usage.Experimental evaluation using various artificial and real-world datasets prove that the various concept drifting algorithms provide highly accurate results in classifying new data instances even in a resource constrained environment, irrespective of size of dataset, type of drift or presence of noise in the dataset.We also present empirically the impact of various features-size of ensemble, period value, threshold value, multiplicative factor and the presence of noise on all the key performance metrics.