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Adaptive XML Stream Classification Using Partial Tree-Edit Distance

  • Dariusz Brzezinski
  • Maciej Piernik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8502)

Abstract

XML classification finds many applications, ranging from data integration to e-commerce. However, existing classification algorithms are designed for static XML collections, while modern information systems frequently deal with streaming data that needs to be processed on-line using limited resources. Furthermore, data stream classifiers have to be able to react to concept drifts, i.e., changes of the streams underlying data distribution. In this paper, we propose XStreamClass, an XML classifier capable of processing streams of documents and reacting to concept drifts. The algorithm combines incremental frequent tree mining with partial tree-edit distance and associative classification. XStreamClass was experimentally compared with four state-of-the-art data stream ensembles and provided best average classification accuracy on real and synthetic datasets simulating different drift scenarios.

Keywords

XML data stream classification concept drift 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Dariusz Brzezinski
    • 1
  • Maciej Piernik
    • 1
  1. 1.Institute of Computing SciencePoznan University of TechnologyPoznanPoland

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