2011 IEEE 11th International Conference on Data Mining (2011)
Dec. 11, 2011 to Dec. 14, 2011
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2011.49
Concept-evolution is one of the major challenges in data stream classification, which occurs when a new class evolves in the stream. This problem remains unaddressed by most state-of-the-art techniques. A recurring class is a special case of concept-evolution. This special case takes place when a class appears in the stream, then disappears for a long time, and again appears. Existing data stream classification techniques that address the concept-evolution problem, wrongly detect the recurring classes as novel class. This creates two main problems. First, much resource is wasted in detecting a recurring class as novel class, because novel class detection is much more computationally- and memory-intensive, as compared to simply recognizing an existing class. Second, when a novel class is identified, human experts are involved in collecting and labeling the instances of that class for future modeling. If a recurrent class is reported as novel class, it will be only a waste of human effort to find out whether it is really a novel class. In this paper, we address the recurring issue, and propose a more realistic novel class detection technique, which remembers a class and identifies it as "not novel" when it reappears after a long disappearance. Our approach has shown significant reduction in classification error over state-of-the-art stream classification techniques on several benchmark data streams.
stream classification, novel class, recurring class
L. Khan et al., "Detecting Recurring and Novel Classes in Concept-Drifting Data Streams," 2011 IEEE 11th International Conference on Data Mining(ICDM), Vancouver, Canada, 2011, pp. 1176-1181.