Associative Classification with Prediction Confidence

  • Tien Dung Do
  • Siu Cheung Hui
  • Alvis C. M. Fong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3930)


Associative classification which uses association rules for classification has achieved high accuracy in comparison with other classification approaches. However, the confidence measure which is conventionally used for selecting association rules for classification may not conform to the prediction accuracy of the rules. In this paper, we propose a measure called prediction confidence to measure the prediction accuracy of association rules. In addition, a probabilistic-based approach for estimating prediction confidence of association rules is given and its performance is evaluated. The use of prediction confidence helps improve the performance of associative classifiers.


Prediction Accuracy Association Rule Class Label Classification Process 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 2006

Authors and Affiliations

  • Tien Dung Do
    • 1
  • Siu Cheung Hui
    • 1
  • Alvis C. M. Fong
    • 1
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingapore

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