A novel online ensemble approach for concept drift in data streams

Data Streams are data instances which arrive at a very rapid rate with varying concepts. Many online ensembles of classifiers were developed which handled the drifting concepts and were proved to be better than a single classifier system. In our work, we will discuss our new approach, Early Dynamic Weighted Majority and will empirically prove it to be better than the existing online ensemble approaches. Empirical results would prove that all these online approaches can be quite competitive, and show good accuracy and speed in handling and identifying drifts in data.