Abstract
In this paper, we propose combining Netconf as quality measure and Dynamic\(-K\) satisfaction mechanism into Class Association Rules (CARs) based classifiers. In our study, we evaluate the use of several quality measures to compute the CARs as well as the main satisfaction mechanisms (“Best Rule”, “Best K Rules” and “All Rules”) commonly used in the literature. Our experiments over several datasets show that our proposal gets the best accuracy in contrast to those reported in state-of-the-art works.
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Clark, P., Boswell, R.: Rule induction with CN2: some recent improvments. In: Proc. of European Working Session on Learning, pp. 151–163 (1991)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proc. of the KDD, pp. 80–86 (1998)
Li, W., Han, J., Pei, J.: CMAR: accurate and efficient classification based on multiple class-association rules. In: Proc. of the ICDM, pp. 369–376 (2001)
Berzal, F., Blanco, I., Sánchez, D., Vila, M.A.: Measuring the accuracy and interest of association rules: A new framework. Intelligent Data Analysis 6(3), 221–235 (2002)
Coenen, F.: The LUCS-KDD discretised/normalised ARM and CARM Data Library (2003). http://www.csc.liv.ac.uk/~frans/KDD/Software/LUCS-KDD-DN
Yin, X., Han, J.: CPAR: Classification based on predictive association rules. In: Proc. of the SIAM International Conference on Data Mining, pp. 331–335 (2003)
Ahn, K.I., Kim, J.Y.: Efficient Mining of Frequent Itemsets and a Measure of Interest for Association Rule Mining. Information and Knowledge Management 3(3), 245–257 (2005). Hanoi, Vietnam
Wang, J., Karypis G.: HARMONY: Efficiently mining the best rules for classification. In: Proc. of SDM, pp. 205–216 (2005)
Coenen, F., Leng, P., Zhang, L.: Threshold Tuning for improved classification association rule mining. In: Ho, T.-B., Cheung, D., Liu, H. (eds.) PAKDD 2005. LNCS (LNAI), vol. 3518, pp. 216–225. Springer, Heidelberg (2005)
Asuncion, A., Newman D.J.: UCI Machine Learning Repository (2007). http://www.ics.uci.edu/~mlearn/MLRepository.html
Wang, Y.J., Xin, Q., Coenen, F.: A novel rule weighting approach in classification association rule mining. In: International Conference on Data Mining Workshops, pp. 271–276 (2007)
Wang, Y.J., Xin, Q., Coenen, F.: A novel rule ordering approach in classification association rule mining. In: Perner, P. (ed.) MLDM 2007. LNCS (LNAI), vol. 4571, pp. 339–348. Springer, Heidelberg (2007)
Cheng, H., Yan, X., Han, J., Yu, P.S.: Direct discriminative pattern mining for effective classification. In: Proc. of the ICDE, pp. 169–178 (2008)
Wang, Y.J., Xin, Q., Coenen, F.: Hybrid Rule Ordering in Classification Association Rule Mining. Trans. MLDM 1(1), 1–15 (2008)
Karabatak, M., Ince, M.C.: An expert system for detection of breast cancer based on association rules and neural network. Expert Syst. Appl. 36, 3465–3469 (2009)
Park, S.H., Reyes, J.A., Gilbert, D.R., Kim, J.W., Kim, S.: Prediction of protein-protein interaction types using association rule based classification. BMC Bioinformatics 10(1) (2009)
Bae, J.K., Kim, J.: Integration of heterogeneous models to predict consumer behavior. Expert Syst. Appl. 37, 1821–1826 (2010)
Hernández, R., Carrasco, J.A., Fco, M.J., Hernández, J.: Classifying using specific rules with high confidence. In: Proc. of the MICAI, pp. 75–80 (2010)
Malik, W.A., Unwin, A.: Automated error detection using association rules. Intelligent Data Analysis 15(5), 749–761 (2011)
Hernández, R., Carrasco, J.A., Fco, M.J., Hernández, J.: CAR-NF: A Classifier based on Specific Rules with High Netconf. Intelligent Data Analysis 16(1), 49–68 (2012)
Hernández-León, R.: Dynamic K: a novel satisfaction mechanism for CAR-based classifiers. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds.) CIARP 2013, Part I. LNCS, vol. 8258, pp. 141–148. Springer, Heidelberg (2013)
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Hernàndez-Leòn, R. (2015). Improving the Accuracy of CAR-based Classifiers by Combining Netconf Measure and Dynamic\(-K\) Mechanism. In: Pardo, A., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2015. Lecture Notes in Computer Science(), vol 9423. Springer, Cham. https://doi.org/10.1007/978-3-319-25751-8_72
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