Classification and Novel Class Detection in Data Streams with Active Mining

  • Mohammad M. Masud
  • Jing Gao
  • Latifur Khan
  • Jiawei Han
  • Bhavani Thuraisingham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6119)


We present ActMiner, which addresses four major challenges to data stream classification, namely, infinite length, concept-drift, concept-evolution, and limited labeled data. Most of the existing data stream classification techniques address only the infinite length and concept-drift problems. Our previous work, MineClass, addresses the concept-evolution problem in addition to addressing the infinite length and concept-drift problems. Concept-evolution occurs in the stream when novel classes arrive. However, most of the existing data stream classification techniques, including MineClass, require that all the instances in a data stream be labeled by human experts and become available for training. This assumption is impractical, since data labeling is both time consuming and costly. Therefore, it is impossible to label a majority of the data points in a high-speed data stream. This scarcity of labeled data naturally leads to poorly trained classifiers. ActMiner actively selects only those data points for labeling for which the expected classification error is high. Therefore, ActMiner extends MineClass, and addresses the limited labeled data problem in addition to addressing the other three problems. It outperforms the state-of-the-art data stream classification techniques that use ten times or more labeled data than ActMiner.


Data Stream Decision Boundary Label Data Concept Drift Class Instance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Mohammad M. Masud
    • 1
  • Jing Gao
    • 2
  • Latifur Khan
    • 1
  • Jiawei Han
    • 2
  • Bhavani Thuraisingham
    • 1
  1. 1.Department of Computer ScienceUniversity of Texas at Dallas 
  2. 2.Department of Computer ScienceUniversity of Illinois at Urbana-Champaign 

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