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2010
IEEE

Addressing Concept-Evolution in Concept-Drifting Data Streams

9 years 1 months ago
Addressing Concept-Evolution in Concept-Drifting Data Streams
Abstract--The problem of data stream classification is challenging because of many practical aspects associated with efficient processing and temporal behavior of the stream. Two such well studied aspects are infinite length and concept-drift. Since a data stream may be considered a continuous process, which is theoretically infinite in length, it is impractical to store and use all the historical data for training. Data streams also frequently experience concept-drift as a result of changes in the underlying concepts. However, another important characteristic of data streams, namely, concept-evolution is rarely addressed in the literature. Concept-evolution occurs as a result of new classes evolving in the stream. This paper addresses concept-evolution in addition to the existing challenges of infinite-length and concept-drift. In this paper, the concept-evolution phenomenon is studied, and the insights are used to construct superior novel class detection techniques. First, we propose...
Mohammad M. Masud, Qing Chen, Latifur Khan, Charu
Added 12 Feb 2011
Updated 12 Feb 2011
Type Journal
Year 2010
Where ICDM
Authors Mohammad M. Masud, Qing Chen, Latifur Khan, Charu C. Aggarwal, Jing Gao, Jiawei Han, Bhavani M. Thuraisingham
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