An Associative Classifier for Uncertain Datasets

  • Metanat Hooshsadat
  • Osmar R. Zaïane
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7301)

Abstract

The classification of uncertain datasets is an emerging research problem that has recently attracted significant attention. Some attempts to devise a classification model with uncertain training data have been proposed using decision trees, neural networks, or other approaches. Among those, the associative classifiers have inspired some of the uncertain classification algorithms given their promising results on standard datasets. We propose a novel associative classifier for uncertain data. Our method, Uncertain Associative Classifier (UAC) is efficient and has an effective rule pruning strategy. Our experimental results on real datasets show that in most cases, UAC reaches better accuracies than the state of the art algorithms.

Keywords

Association Rule Class Label Test Instance Relative Precedence Association Rule Mining 
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 2012

Authors and Affiliations

  • Metanat Hooshsadat
    • 1
  • Osmar R. Zaïane
    • 1
  1. 1.University of AlbertaEdmontonCanada

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