Abstract
This paper presents a novel classification approach that integrates fuzzy class association rules and support vector machines. A fuzzy discretization technique is applied to transform the training set, particularly quantitative attributes, to a format appropriate for association rule mining. A hill-climbing procedure is adapted for automatic thresholds adjustment and fuzzy class association rules are mined accordingly. The compatibility between the generated rules and patterns is considered to construct a set of feature vectors, which are used to generate a classifier. The reported test results show that compatible rule-based feature vectors present a highly-qualified source of discrimination knowledge that can substantially impact the prediction power of the final classifier.
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References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rule. In: Proc. of VLDB (1994)
Cristianini, N., Taylor, J.S.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge (2000)
Coenen, F.P., Leng, P.: The Effect of Threshold Values on Association Rule Based Classification Accuracy. DKE 60(2), 345–360 (2007)
Coenen, F.P.: The LUCS-KDD TFPC Classification Association Rule Mining Algorithm, University of Liverpool (2004), www.cSc.liv.ac.uk/~frans/KDD/Software/Apriori_TFPC/aprioriTFPC.html
Cong, G., et al.: Mining Top-k Covering Rule Groups for Gene Expression Data. In: Proc. of ACM SIGMOD, pp. 670–681 (2005)
Chang, C.C., Lin, C.J.: LIBSVM: A Library for Support Vector Machines (2001), www.csie.ntu.edu.tw/~cjlin/libsvm
Furey, T.S., et al.: Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16 (2000)
Li, W., Han, J., Pei, J.: CMAR: Accurate and efficient classification based on multiple class-association rules. In: Proc. of IEEE ICDM (2001)
Liu, B., Hsu, W., Ma, Y.: Integrating Classification and Association Rule Mining. In: Proc. of ACM KDD (1998)
Ishibuchi, H., Nakashima, T.: Improving the performance of fuzzy classifier systems for pattern classification problems with continuous attributes. IEEE Trans. Industrial Electronics 46(6), 157–168 (1999)
Ishibuchi, H., Nozaki, K., Tanaka, H.: Distributed Representation of Fuzzy Rules and Its Application to Pattern Classification. Fuzzy Sets and Systems 52(1), 21–32 (1992)
Kianmehr, K., Alhajj, R.: Effective Classification by Integrating Support Vector Machine and Association Rule Mining. In: Corchado, E.S., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 920–927. Springer, Heidelberg (2006)
Kianmehr, K., Alhajj, R.: Support Vector Machine Approach for Fast Classification. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2006. LNCS, vol. 4081, pp. 534–543. Springer, Heidelberg (2006)
Merz, C.J., Murphy, P.: UCI repository of machine learning database (1996), http://www.cs.uci.edu/~mlearn/MLRepository.html
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Kianmehr, K., Alshalalfa, M., Alhajj, R. (2008). Effectiveness of Fuzzy Discretization for Class Association Rule-Based Classification. In: An, A., Matwin, S., Raś, Z.W., Ślęzak, D. (eds) Foundations of Intelligent Systems. ISMIS 2008. Lecture Notes in Computer Science(), vol 4994. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68123-6_33
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DOI: https://doi.org/10.1007/978-3-540-68123-6_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68122-9
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