Ensembles of Heterogeneous Concept Drift Detectors - Experimental Study
For the contemporary enterprises, possibility of appropriate business decision making on the basis of the knowledge hidden in stored data is the critical success factor. Therefore, the decision support software should take into consideration that data usually comes continuously in the form of so-called data stream, but 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 confirm its pretty good quality, what encourage us to use it in practical applications.
KeywordsData stream Concept drift Pattern classification Drift detector
This work was supported by the statutory funds of the Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Science and Technology and by the Polish National Science Centre under the grant No. DEC-2013/09/B/ST6/02264. This work was also supported by the AGH Statutory Funds No. 11.11.230.017. All computer experiments were carried out using computer equipment sponsored by ENGINE project (http://engine.pwr.edu.pl/).
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