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PKDD
2010
Springer

Classification and Novel Class Detection of Data Streams in a Dynamic Feature Space

8 years 6 months ago
Classification and Novel Class Detection of Data Streams in a Dynamic Feature Space
Data stream classification poses many challenges, most of which are not addressed by the state-of-the-art. We present DXMiner, which addresses four major challenges to data stream classification, namely, infinite length, concept-drift, concept-evolution, and feature-evolution. Data streams are assumed to be infinite in length, which necessitates single-pass incremental learning techniques. Concept-drift occurs in a data stream when the underlying concept changes over time. Most existing data stream classification techniques address only the infinite length and concept-drift problems. However, concept-evolution and feature- evolution are also major challenges, and these are ignored by most of the existing approaches. Concept-evolution occurs in the stream when novel classes arrive, and feature-evolution occurs when new features emerge in the stream. Our previous work addresses the concept-evolution problem in addition to addressing the infinite length and concept-drift problems. Most of...
Mohammad M. Masud, Qing Chen, Jing Gao, Latifur Kh
Added 14 Feb 2011
Updated 14 Feb 2011
Type Journal
Year 2010
Where PKDD
Authors Mohammad M. Masud, Qing Chen, Jing Gao, Latifur Khan, Jiawei Han, Bhavani M. Thuraisingham
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