Classification and adaptive novel class detection of feature-evolving data streams

Mohammad M. Masud, Qing Chen, Latifur Khan, Charu C. Aggarwal, Jing Gao, Jiawei Han, Ashok Srivastava, Nikunj C. Oza

Research output: Contribution to journalArticle

Abstract

Data stream classification poses many challenges to the data mining community. In this paper, we address four such major challenges, namely, infinite length, concept-drift, concept-evolution, and feature-evolution. Since a data stream is theoretically infinite in length, it is impractical to store and use all the historical data for training. Concept-drift is a common phenomenon in data streams, which occurs as a result of changes in the underlying concepts. Concept-evolution occurs as a result of new classes evolving in the stream. Feature-evolution is a frequently occurring process in many streams, such as text streams, in which new features (i.e., words or phrases) appear as the stream progresses. Most existing data stream classification techniques address only the first two challenges, and ignore the latter two. In this paper, we propose an ensemble classification framework, where each classifier is equipped with a novel class detector, to address concept-drift and concept-evolution. To address feature-evolution, we propose a feature set homogenization technique. We also enhance the novel class detection module by making it more adaptive to the evolving stream, and enabling it to detect more than one novel class at a time. Comparison with state-of-the-art data stream classification techniques establishes the effectiveness of the proposed approach.

Original languageEnglish (US)
Article number6205751
Pages (from-to)1484-1497
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume25
Issue number7
DOIs
StatePublished - Jun 3 2013

Keywords

  • Data stream
  • concept-evolution
  • novel class
  • outlier

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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  • Cite this

    Masud, M. M., Chen, Q., Khan, L., Aggarwal, C. C., Gao, J., Han, J., Srivastava, A., & Oza, N. C. (2013). Classification and adaptive novel class detection of feature-evolving data streams. IEEE Transactions on Knowledge and Data Engineering, 25(7), 1484-1497. [6205751]. https://doi.org/10.1109/TKDE.2012.109