A first attempt to construct effective concept drift detector ensembles

Abstract

The big data is usually described by so-called 5Vs (Volume, Velocity, Variety, Veracity, Value). The business success in the big data era strongly depends on the smart analytical software which can help to make efficient decisions (Value for enterprise). Therefore, the decision support software should take into consideration especially that we deal with massive data (Volume) and that data usually comes continuously in the form of so-called data stream (Velocity). Unfortunately, most of the traditional data analysis methods are not ready to efficiently analyze fast growing amount of the stored records. Additionally, one should also consider phenomenon appearing in data stream called concept drift, which means that the parameters of an using model are changing, what could dramatically decrease the analytical model quality. This work is focusing on the classification task, which is very popular in many practical cases as fraud detection, network security, or medical diagnosis. We propose how to detect the changes in the data stream using combined concept drift detection model. The experimental evaluations show that it is an interesting direction, what encourage us to use it in practical applications.