Addressing concept-evolution in concept-drifting data streams

Mohammad M. Masud, Qing Chen, Latifur Khan, Charu Aggarwal, Jing Gao, Jiawei Han, Bhavani Thuraisingham

Research output: Chapter in Book/Report/Conference proceedingConference contribution

99 Citations (Scopus)


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 an adaptive threshold for outlier detection, which is a vital part of novel class detection. Second, we propose a probabilistic approach for novel class detection using discrete Gini Coefficient, and prove its effectiveness both theoretically and empirically. Finally, we address the issue of simultaneous multiple novel class occurrence, and provide an elegant solution to detect more than one novel classes at the same time. We also consider feature-evolution in text data streams, which occurs because new features (i.e., words) evolve in the stream. Comparison with state-of-the-art data stream classification techniques establishes the effectiveness of the proposed approach.

Original languageEnglish
Title of host publicationProceedings - 10th IEEE International Conference on Data Mining, ICDM 2010
Number of pages6
Publication statusPublished - 2010
Externally publishedYes
Event10th IEEE International Conference on Data Mining, ICDM 2010 - Sydney, NSW, Australia
Duration: Dec 14 2010Dec 17 2010

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Other10th IEEE International Conference on Data Mining, ICDM 2010
CitySydney, NSW


  • Concept-evolution
  • Data stream
  • Novel class
  • Outlier

ASJC Scopus subject areas

  • General Engineering


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