Empirical Support for Weighted Majority, Early Drift Detection Method and Dynamic Weighted Majority

Concept drift is the recent trend of online data. The distribution underlying the data is changing with time. There are many algorithms developed in the literature to handle such drifting data concepts. In our paper we will experimentally compare the three different types of concept drifting algorithms, Weighted Majority, EDDM and DWM on datasets that contain different types of concept drift. Here, we will prove that these algorithms can be quite competitive practically, and can improve the accuracy and speed in handling and identifying drifts in data. We have also discussed the case of DWM and calculated the theoretical bounds for the experts´ weight reduction when an expert makes a mistake in classifying a new instance.