Mining axiomatic fuzzy set association rules for classification problems
AbstractIn 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Elsevier in its journal European Journal of Operational Research.
Volume (Year): 218 (2012)
Issue (Month): 1 ()
Contact details of provider:
Web page: http://www.elsevier.com/locate/eor
Data mining; Fuzzy association rules; AFS fuzzy logic; Knowledge acquisition; Classification;
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei).
If references are entirely missing, you can add them using this form.