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2012 IEEE 12th International Conference on Data Mining (2012)
Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 13, 2012
ISSN: 1550-4786
ISBN: 978-1-4673-4649-8
pp: 31-40
ABSTRACT
Concept-evolution has recently received a lot of attention in the context of mining data streams. Concept-evolution occurs when a new class evolves in the stream. Although many recent studies address this issue, most of them do not consider the scenario of recurring classes in the stream. A class is called recurring if it appears in the stream, disappears for a while, and then reappears again. Existing data stream classification techniques either misclassify the recurring class instances as another class, or falsely identify the recurring classes as novel. This increases the prediction error of the classifiers, and in some cases causes unnecessary waste in memory and computational resources. In this paper we address the recurring class issue by proposing a novel "class-based" ensemble technique, which substitutes the traditional "chunk-based" ensemble approaches and correctly distinguishes between a recurring class and a novel one. We analytically and experimentally confirm the superiority of our method over state-of-the-art techniques.
INDEX TERMS
recurring class, stream classification, novel class
CITATION

T. Al-Khateeb, M. M. Masud, L. Khan, C. Aggarwal, J. Han and B. Thuraisingham, "Stream Classification with Recurring and Novel Class Detection Using Class-Based Ensemble," 2012 IEEE 12th International Conference on Data Mining(ICDM), Brussels, Belgium Belgium, 2012, pp. 31-40.
doi:10.1109/ICDM.2012.125
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