Algorithms for Filtration of Unordered Sets of Regression Rules

  • Łukasz Wróbel
  • Marek Sikora
  • Adam Skowron
Conference paper

DOI: 10.1007/978-3-642-35455-7_26

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7694)
Cite this paper as:
Wróbel Ł., Sikora M., Skowron A. (2012) Algorithms for Filtration of Unordered Sets of Regression Rules. In: Sombattheera C., Loi N.K., Wankar R., Quan T. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2012. Lecture Notes in Computer Science, vol 7694. Springer, Berlin, Heidelberg

Abstract

This paper presents six filtration algorithms for the pruning of the unordered sets of regression rules. Three of these algorithms aim at the elimination of the rules which cover similar subsets of examples, whereas the other three ones aim at the optimization of the rule sets according to the prediction accuracy. The effectiveness of the filtration algorithms was empirically tested for 5 different rule learning heuristics on 35 benchmark datasets. The results show that, depending on the filtration algorithm, the reduction of the number of rules fluctuates on average between 10% and 50% and in most cases it does not cause statistically significant degradation in the accuracy of predictions.

Keywords

rule-based regression rule induction rule filtration rule quality measures 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Łukasz Wróbel
    • 1
  • Marek Sikora
    • 1
    • 2
  • Adam Skowron
    • 1
  1. 1.Institute of Computer ScienceSilesian University of TechnologyGliwicePoland
  2. 2.Institute of Innovative Technologies EMAGKatowicePoland

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