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Efficient Pruning Methods for Obtaining Compact Associative Classifiers with Enhanced Classification Accuracy Rate

  • Kavita MittalEmail author
  • Gaurav Aggarwal
  • Prerna Mahajan
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1025)

Abstract

The integrated approach involving supervised classification and association rule mining for development of classification model is becoming a promising strategy for building compact and accurate classifiers. As discussed in literature, in large databases the association rule mining techniques may produce large rule sets, this paper attempts to propose and implement several pruning methods to overcome the problems underlying the Associative Classification approach. The aim is to efficiently utilize the rules produced for classification in compact form and represent a rule set to be the part of classifier model with maximum data coverage and enhanced accuracy. This paper also presents experimental evaluation of the pruning methods on various datasets taken from UCI machine learning repository with the consideration of earlier approaches.

Keywords

Association Classification (AC) Pruning Full rule matching Partial rule matching Class correctness Prediction Classifier Classification accuracy 

References

  1. Kliegr, T. (2017): Quantitative CBA: Small and Comprehensible Association Rule Classification Models arXiv:1711.10166. (eprint arXiv:1711.10166)
  2. Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proceedings of the KDD, pp. 80–86. New York (1998)Google Scholar
  3. Merz, C., Murphy, P.: UCI Repository of Machine Learning Databases. University of California, Department of Information and Computer Science, Irvine (1996)Google Scholar
  4. Mittal, K., Aggarwal, G., Mahajan, P.: Int. J. Inf. Tecnol. 1–6 (2018).  https://doi.org/10.1007/s41870-018-0233-xCrossRefGoogle Scholar
  5. Zaïane, O.R., Antonie, M.-L.: On pruning and tuning rules for associative classifiers. KES 3, 966–973 (2005)Google Scholar
  6. Garza, P., Chiusano, S., Baralis, E.: A lazy approach to associative classification. In: IEEE Transactions on Knowledge & Data Engineering, vol. 20, pp. 156–171 (2007).  https://doi.org/10.1109/tkde.2007.190677CrossRefGoogle Scholar
  7. Sahu, S.K., Kumar, P., Singh, A.P.: Modified K-NN algorithm problems for classification with improved accuracy. Int. J. Inf. Technol. 10, 65 (2018).  https://doi.org/10.1007/s41870-017-0058CrossRefGoogle Scholar
  8. Thabtah, F.: Pruning techniques in associative classification: survey and comparison. JDIM 4, 197–202 (2006)Google Scholar
  9. Zhao, M., Cheng, X., He, Q.: An algorithm of mining class association rules. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds.) Advances in Computation and Intelligence, vol. 5821. Springer, Berlin (2009).  https://doi.org/10.1007/978-3-642-04843-2_29CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.JaganNath UniversityBahadurgarhIndia
  2. 2.Institute of Information Technology and ManagementNew DelhiIndia

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