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...

• Type: Journal
• Year: 2010
• Where: PKDD
• Authors: Mohammad M. Masud, Qing Chen, Jing Gao, Latifur Khan, Jiawei Han, Bhavani M. Thuraisingham
• Keywords: Classification Techniques, Data Mining, Data Streams, PKDD 2010, Stream