Advertisement

Neural Computing and Applications

, Volume 31, Issue 3, pp 895–908 | Cite as

RACER: accurate and efficient classification based on rule aggregation approach

  • Javad BasiriEmail author
  • Fattaneh Taghiyareh
  • Heshaam Faili
Original Article
  • 62 Downloads

Abstract

Rule-based classification is one of the most important topics in the field of data mining due to its wide applications. This article presents a novel rule-based classifier called RACER (Rule Aggregating ClassifiER) to improve the accuracy of data classification. RACER uses a specific rule representation that enables it to consider each instance in the training data as an initial rule, without spending any cost. In order to retrieve an applicable rule set, RACER tries to combine the initial rules together. If the combined rule has a better fitness value in comparison with the two input rules, RACER combines them together. We have used seventeen different datasets from UCI machine learning database repository to evaluate RACER’s capability in classifying various kinds of databases. Moreover, to assess RACER’s performance, we compared our results with some other well-known classifiers including CN.2, PART, C4.5 and SVM. Our experiments show that RACER is an effective classifier in various domains and has better average classification accuracy and understandability in comparison with other applied classifiers.

Keywords

RACER Data mining Classification Rule-based classifier 

Notes

Compliance with ethical standards

Conflict of interest

All the authors declare that there is no conflict of interest and that no financial support has been received that could have influenced its outcome.

References

  1. 1.
    Tseng V, Lee C (2009) Effective temporal data classification by integrating sequential pattern mining and probabilistic induction. Expert Syst Appl 36(5):9524–9532Google Scholar
  2. 2.
    Yang Y, Slattery S, Ghani R (2002) A study of approaches to hypertext categorization. J Intell Inf Syst 18(2):219–241Google Scholar
  3. 3.
    Ngai E, Xiu L, Chau D (2009) Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst Appl 36(22):2592–2602Google Scholar
  4. 4.
    Basiri J, Taghiyareh F (2012) An application of the CORER classifier on customer churn prediction. In: Telecommunications (IST), 2012 sixth international symposium on 2012 Nov 6. IEEE, pp 867–872Google Scholar
  5. 5.
    Siami M, Gholamian MR, Basiri J (2014) An application of locally linear model tree algorithm with combination of feature selection in credit scoring. Int J Syst Sci 45(10):2213–2222MathSciNetzbMATHGoogle Scholar
  6. 6.
    Frank E, Witten I (1998) Generating accurate rule sets without global optimization. In: Fifteenth international conference on machine learning. pp 144–151Google Scholar
  7. 7.
    Clark P, Niblett T (1989) The CN2 induction algorithm. Mach Learn 3(4):261–283Google Scholar
  8. 8.
    Mastrogiannis N, Boutsinas B, Giannikos I (2009) A method for improving the accuracy of data mining classification algorithms. Comput Oper Res 36(10):2829–2839zbMATHGoogle Scholar
  9. 9.
    Quinlan J (1993) C4.5: programs for machine learning. Morgan Kaufmann, San MateoGoogle Scholar
  10. 10.
    Quinlan J (1986) Induction of decision trees. Mach Learn 1(1):81–106Google Scholar
  11. 11.
    Siami M, Gholamian MR, Basiri J, Fathian M (20111) An application of locally linear model tree algorithm for predictive accuracy of credit scoring. In: International conference on model and data engineering 2011 Sep 28. Springer, Berlin Heidelberg, pp 133–142Google Scholar
  12. 12.
    Domingos P.M. (1996) Efficient specific-to-general rule induction. In: KDD. pp 319–322)Google Scholar
  13. 13.
    Basiri J, Taghiyareh F, Gazani S (2010) CORER: a new rule generator classifier. 13th IEEE CSEGoogle Scholar
  14. 14.
    John GH, Langley P (1995) Estimating continuous distributions in Bayesian classifiers. In: Eleventh conference on uncertainty in artificial intelligence. pp 338–345Google Scholar
  15. 15.
    Rumelhart D, Hinton G, Williams R (1985) Learning internal representations by error propagation. In: Parallel distributed processing: explorations in the microstructure of cognition, vol 1. pp 318–362Google Scholar
  16. 16.
    Zhang Y, Xie F, Huang D, Ji M (2010) Support vector classifier based on fuzzy c-means and Mahalanobis distance. J Intell Inf Syst 35(2):333–345Google Scholar
  17. 17.
    Quinlan R (2005) Data mining tools. <http://www.rulequest.com/see5-info.html>
  18. 18.
    Li W, Han J, Pei J (2001) CMAR: accurate and efficient classification based on multiple class-association rules. In: IEEE international conference on data mining. pp 369–376Google Scholar
  19. 19.
    Wang K, Zhou S, He Y (2000) Growing decision trees on support-less association rules. In: Proceedings of the Sixth ACM SIGKDD international conference on knowledge discovery and data mining. pp 265–269Google Scholar
  20. 20.
    Dehuri S, Mall R (2006) Predictive and comprehensible rule discovery using a multi-objective genetic algorithm. Knowl-Based Syst 19:413–421Google Scholar
  21. 21.
    Ceci M, Appice A (2006) Spatial associative classification: propositional vs structural approach. J Intell Inf Syst 27(3):191–213Google Scholar
  22. 22.
    Flouvat F, De Marchi F, Petit JM (2010) A new classification of datasets for frequent itemsets. J Intell Inf Syst 34(1):1–19Google Scholar
  23. 23.
    Shaharanee INM, Hadzic F, Dillon TS (2011) Interestingness measures for association rules based on statistical validity. Knowl-Based Syst 24:386–392Google Scholar
  24. 24.
    Han J, Kamber M (2000) Data mining: concepts and techniques. Morgan Kaufmann Publishers Inc, San Francisco, CAzbMATHGoogle Scholar
  25. 25.
    De Jong KA, Spears WM, Gordon DF (1993) Using genetic algorithms for concept learning. Mach Learn 13(2):161–188Google Scholar
  26. 26.
    UCI machine learning repository. Available at: http://archive.ics.uci.edu/ml/
  27. 27.
    Basiri J, Taghiyareh F, Moshiri B (2010) A hybrid approach to predict churn. In: Services computing conference (APSCC), 2010 IEEE Asia-Pacific 2010 Dec 6. IEEE, pp. 485–491Google Scholar
  28. 28.
    Witten H, Frank E (2005) Data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann <http://www.cs.waikato.ac.nz/ml/weka>
  29. 29.
    Demsar J, Zupan B (2004)Orange: from experimental ma-chine learning to interactive data mining. (White paper) http://www.ailab.si/orange
  30. 30.
    Kianmehr K, Alhajj R (2008) CARSVM: a class association rule-based classification framework and its application to gene expression data. Artif Intell Med 44(1):7–25Google Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.University of TehranTehranIran

Personalised recommendations