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Mining axiomatic fuzzy set association rules for classification problems

Listed author(s):
  • Wang, Xin
  • Liu, Xiaodong
  • Pedrycz, Witold
  • Zhu, Xiaolei
  • Hu, Guangfei
Registered author(s):

    In this paper, we propose a novel method to mine association rules for classification problems namely AFSRC (AFS association rules for classification) realized in the framework of the axiomatic fuzzy set (AFS) theory. This model provides a simple and efficient rule generation mechanism. It can also retain meaningful rules for imbalanced classes by fuzzifying the concept of the class support of a rule. In addition, AFSRC can handle different data types occurring simultaneously. Furthermore, the new model can produce membership functions automatically by processing available data. An extensive suite of experiments are reported which offer a comprehensive comparison of the performance of the method with the performance of some other methods available in the literature. The experimental result shows that AFSRC outperforms most of other methods when being quantified in terms of accuracy and interpretability. AFSRC forms a classifier with high accuracy and more interpretable rule base of smaller size while retaining a sound balance between these two characteristics.

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    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 218 (2012)
    Issue (Month): 1 ()
    Pages: 202-210

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    Handle: RePEc:eee:ejores:v:218:y:2012:i:1:p:202-210
    DOI: 10.1016/j.ejor.2011.04.022
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    1. Unler, Alper & Murat, Alper, 2010. "A discrete particle swarm optimization method for feature selection in binary classification problems," European Journal of Operational Research, Elsevier, vol. 206(3), pages 528-539, November.
    2. Dunbar, Michelle & Murray, John M. & Cysique, Lucette A. & Brew, Bruce J. & Jeyakumar, Vaithilingam, 2010. "Simultaneous classification and feature selection via convex quadratic programming with application to HIV-associated neurocognitive disorder assessment," European Journal of Operational Research, Elsevier, vol. 206(2), pages 470-478, October.
    3. Ravi, V. & Zimmermann, H. -J., 2000. "Fuzzy rule based classification with FeatureSelector and modified threshold accepting," European Journal of Operational Research, Elsevier, vol. 123(1), pages 16-28, May.
    4. Ravi, V. & Reddy, P. J. & Zimmermann, H. -J., 2000. "Pattern classification with principal component analysis and fuzzy rule bases," European Journal of Operational Research, Elsevier, vol. 126(3), pages 526-533, November.
    5. Amo, A. & Montero, J. & Biging, G. & Cutello, V., 2004. "Fuzzy classification systems," European Journal of Operational Research, Elsevier, vol. 156(2), pages 495-507, July.
    6. Meiri, Ronen & Zahavi, Jacob, 2006. "Using simulated annealing to optimize the feature selection problem in marketing applications," European Journal of Operational Research, Elsevier, vol. 171(3), pages 842-858, June.
    7. Lenca, Philippe & Meyer, Patrick & Vaillant, Benoit & Lallich, Stephane, 2008. "On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid," European Journal of Operational Research, Elsevier, vol. 184(2), pages 610-626, January.
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