Application of Combined Classifiers to Data Stream ClassificationReport as inadecuate

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1 Department of Systems and Computer Networks Wroclaw

Abstract : The progress of computer science caused that many institutions collected huge amount of data, which analysis is impossible by human beings. Nowadays simple methods of data analysis are not sufficient for efficient management of an average enterprize, since for smart decisions the knowledge hidden in data is highly required, as which multiple classifier systems are recently the focus of intense research. Unfortunately the great disadvantage of traditional classification methods is that they -assume- that statistical properties of the discovered concept which model is predicted are being unchanged. In real situation we could observe so-called concept drift, which could be caused by changes in the probabilities of classes or-and conditional probability distributions of classes. The potential for considering new training data is an important feature of machine learning methods used in security applications or marketing departments. Unfortunately, the occurrence of this phenomena dramatically decreases classification accuracy.

Keywords : machine learning classifier ensemble data stream concept drift incremental learning forgetting

Author: Michał Woźniak -



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