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An Evolutionary Algorithm for Automated Discovery of Small-Disjunct Rules

by Basheer M. Al-maqaleh, Mohammed A. Al-dohbai, Hamid Shahbazkia
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 41 - Number 8
Year of Publication: 2012
Authors: Basheer M. Al-maqaleh, Mohammed A. Al-dohbai, Hamid Shahbazkia
10.5120/5563-7643

Basheer M. Al-maqaleh, Mohammed A. Al-dohbai, Hamid Shahbazkia . An Evolutionary Algorithm for Automated Discovery of Small-Disjunct Rules. International Journal of Computer Applications. 41, 8 ( March 2012), 33-37. DOI=10.5120/5563-7643

@article{ 10.5120/5563-7643,
author = { Basheer M. Al-maqaleh, Mohammed A. Al-dohbai, Hamid Shahbazkia },
title = { An Evolutionary Algorithm for Automated Discovery of Small-Disjunct Rules },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 41 },
number = { 8 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 33-37 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume41/number8/5563-7643/ },
doi = { 10.5120/5563-7643 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:29:05.802210+05:30
%A Basheer M. Al-maqaleh
%A Mohammed A. Al-dohbai
%A Hamid Shahbazkia
%T An Evolutionary Algorithm for Automated Discovery of Small-Disjunct Rules
%J International Journal of Computer Applications
%@ 0975-8887
%V 41
%N 8
%P 33-37
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In general rule induction algorithms have a bias that favors the discovery of large disjuncts, rather than small disjuncts. In the context of data mining, small disjuncts are rules covering a small number of examples. Due to their nature, small disjuncts are error prone. It correctly classify individually only few examples but, collectively, cover a significant percentage of the set of examples, so that it is important to develop new approaches to cope with the problem of small disjuncts. This paper presents a classification algorithm based on Evolutionary Algorithm (EA) that discovers interesting small-disjunct rules in the form If P Then D. The proposed system specifically designed for discovering rules covering examples belonging to small disjuncts. The proposed algorithm is validated on several datasets of UCI data set repository and the experimental results are presented to demonstrate the effectiveness of the proposed scheme for automated small-disjunct rules mining.

References
  1. Fayyad, U. M. , Piatetsky-Sharpio, G. , and Smyth, P. 1996. From mining to knowledge discovery : An overview. In: U. M. Fayyad G. Piatetsky-Sharpio, P. Smyth and R. Uthurusany (Eds. )Advances in knowledge discovery and data mining ,AAAI/MIT Press, pp. 1-34.
  2. Bramer, M. 2007. Principles of Data Mining. Springer-Verlag London Limited.
  3. Goldberg, D. E. 1989. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesely.
  4. Frietas, A. A. 2002. Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer-Verlag Berlin Heidelberg.
  5. Quinlan, J. R. 1993. C4. 5: Programs for Machine Learning. Morgan Kaufmann.
  6. Dehuri, S. , Patnaik, S. , Ghosh, A. , and Mall, R. 2008. Application of elitist multi-objective genetic algorithm for classification rule generation. Applied Soft Computing, vol. 8, pp. 477–487.
  7. Carvalho, D. R. and Frietas, A. A. 2002. A genetic algorithm for discovering small-disjunct rules in data mining. Applied Soft Computing, vol. 2, no. 1, pp. 75-88.
  8. Danyluk, A. P. and Provost, F. J. 1993. Small disjuncts in action: Learning to diagnose errors in the local loop of the telephone network. In Proceedings of 10th International Conference Machine Learning, pp. 81-88,1993.
  9. Carvalho, D. R. and Frietas, A. A. 2002. A genetic algorithm with sequential niching for discovering small-disjunct rules. In the Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'2002), New York, pp. 1035-1042.
  10. Yogita, Saroj, and Kumar, D. 2009. Rule +Exceptions: Automated discovery of comprehensible decision rules. IEEE International Advance Computing Conference(IACC2009),Patiala, India, pp. 1479-1483.
  11. Carvalho, D. R. and Freitas, A. A. 2000. A genetic algorithm-based solution for the problem of small disjuncts. Principles of data mining and knowledge discovery. In Proceedings of the 4th European Conference, PKDD-2000, Lyon, France. Lecture Notes in Artificial Intelligence 1910, Springer, Berlin, pp. 345-352.
  12. Carvalho, D. R. and Freitas, A. A. 2000. A hybrid decision-tree/genetic algorithm for coping with the problem of small disjuncts in data mining. In Proceedings of the 2000 Genetic and Evolutionary Computation Conference (Gecco-2000), Las Vegas, NV, USA, pp. 1061–1068.
  13. Sarkar, B. K. , Sana, S. S. , and Chaudhuri, K. 2012. A genetic algorithm-based rule extraction system", Applied Soft Computing, vol. 12, pp. 238-254.
  14. Bharadwaj, K. K. and Al-Maqaleh, B. M. 2006. Evolutionary approach for automated discovery of augmented production rules. International Journal of Computational Intelligence, vol. 3, Issue 4, pp. 267-275.
  15. Al-Maqaleh, B. M. and Bharadwaj, K. K. 2007. Evolutionary approach to automated discovery of censored production rules with fuzzy hierarchy. In Proceedings of the International Conference on Data Mining and Applications (ICDMA'2007), Hong Kong, China, vol. 1, pp. 716-721.
  16. Dehuri, S. and Mall, R. 2006. Predictive and comprehensible rule discovery using a multi objective genetic algorithms. Knowledge Based Systems, vol. 19, pp. 413-421.
  17. Liu, B. , Hu, M. and Hsu, W. 2000. Multi-level organization and summarization of the discovered rules. In Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovered and Data Mining (KDD-2000), ACM Press, pp. 208–220.
  18. Weiss, G. M. and Hirsh, H. 2000. A quantitative study of small disjuncts. In Proceedings of the of 17th National Conference on Artificial Intelligence (AAAI-2000), Austin, TX, pp. 665– 670.
  19. Gomes, A. K. 2007. Small disjuncts grouping by rule coverage and accuracy measures. In 7th IEEE International Conference on Intelligent Systems Design and Applications, pp. 412-415.
  20. Weiss, G. M. 1998. The problem with noise and small disjuncts. In Proceedings of the International Conference on Machine Learning (ICML-98), Morgan Kaufmann, Los Altos, CA, pp. 574–578.
  21. Holte, R. C. , Acker, L. E. , and Porter, B. W. 1989. Concept learning and the problem of small disjuncts. In Proceedings of IJCAI – 89, pp. 813-818.
  22. Weiss, G. W. 1995. Learning with rare cases and small disjuncts. In Proceedings of the 12th International Conference on Machine Learning (ICML-95), Morgan Kaufmann, Los Altos, CA, pp. 558–565.
  23. Ting, K. M. 1994. The problem of small disjuncts: its remedy in decision trees. In Proceedings of the 10th Canadian Conference on AI, pp. 91-97.
  24. Saroj, R. and Bharadwaj, K. K. 2009. Discovery of exceptions: A step towards perfection. Third IEEE International Conference on Network and System Security, Banaras Hindu University, India, pp. 540-545.
  25. Al-Maqaleh, B. M. 2012. Genetic algorithm approach to automated discovery of comprehensible production rules. In the proceedings of the 2nd International Conference on Advanced Computing & Communication Technologies(ACCT2012), Rohtak, India, pp. 69-71.
  26. Thornton, C. J. 1992. Techniques in Computational Learning-An Introduction. London: Chapman & Hall.
  27. Witten, I. H. , Frank, E. 2005 Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, 2nd edition. Morgan Kaufmann.
  28. UCI Repository of Machine Learning Databases, Department of Information and Computer Science University of California, 1994. [http://www. ics. uci. edu/ ~mlearn/MLRepositry. html].
  29. Quinlan, J. R. 1991. Improved estimates for the accuracy of small disjuncts. Journal of Machine Learning, Kluwer Academic Publishers Hingham, MA, USA, vol. 6(1), pp. 93-98.
Index Terms

Computer Science
Information Sciences

Keywords

Interestingness Small Disjunct Predictive Accuracy Genetic Algorithm

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