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Knowledge Acquisition tool for Classification Rules using Genetic Algorithm Approach

by Fadl Mutaher Ba-alwi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 60 - Number 1
Year of Publication: 2012
Authors: Fadl Mutaher Ba-alwi
10.5120/9658-4078

Fadl Mutaher Ba-alwi . Knowledge Acquisition tool for Classification Rules using Genetic Algorithm Approach. International Journal of Computer Applications. 60, 1 ( December 2012), 29-35. DOI=10.5120/9658-4078

@article{ 10.5120/9658-4078,
author = { Fadl Mutaher Ba-alwi },
title = { Knowledge Acquisition tool for Classification Rules using Genetic Algorithm Approach },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 60 },
number = { 1 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 29-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume60/number1/9658-4078/ },
doi = { 10.5120/9658-4078 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:05:31.018788+05:30
%A Fadl Mutaher Ba-alwi
%T Knowledge Acquisition tool for Classification Rules using Genetic Algorithm Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 60
%N 1
%P 29-35
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Classification Rule Mining (CRM) is a data mining technique for discovering important classification rules from large dataset. This work presents an efficient genetic algorithm for discovering significant IF-THEN rules from a given dataset. The proposed algorithm consists of two main steps. First step generates set of classification rules and the second step deletes the weak rules and selects only the significant rules. Since weak rules are deleted and significant rules are selected, the proposed algorithm can be considered as knowledge acquisition tool for classification problems. Experimental results are presented to demonstrate the contribution of the proposed algorithm for discovering the significant rules.

References
  1. Han, J. , Kamber, M. and Pei, J. 2011 Data Mining: Concepts and Techniques. Third Edition, Morgan Kaufmann.
  2. Wang, Y. J. , Xin, Q. , and Coenen, F. , 2008 Mining Efficiently Significant Classification Association rules. Data Mining: Foundations and Practice, 443-467.
  3. Freitas, A. A. , 2002 Data Mining and Knowledge Discovery with Evolutionary Algorithms. Springer-Verlag, Berlin Heidelberg
  4. Liu, B. , Hsu, W. , and Chen, S. , 1997 Using General Impressions to Analyze Discovered Classification Rules. In Proc. KDD97 3rd International Conference on Knowledge Discovery and Data Mining, AAAI Press, 31–36.
  5. Zhang, H. , Padmanabhan, B. , and Tuzhilin, A. , 2004 On the discovery of significant statistical quantitative rules. In Proc. of the 10th international conference on knowledge discovery and data mining, KDD-2004, 374-383.
  6. Wang, Y. J. , Xin, Q. , and Coenen, F. , 2005 Selection of Significant Rules in Classification Association Rule Mining. In Proc. of ICDM2005 WORKSHOP on Foundations of Semantic Oriented Data and Web Mining (ICDM-FDM'2005), 106-108.
  7. Webb, G. I. , 2006 Discovering significant rules. In Proc. of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, KDD-2006, 434-443.
  8. Tzacheva, A. , and Ra?, Z. W. , 2007 Constraint Based Action Rule Discovery with Single Classification Rules. Intelligent Data Engineering and Automated Learning, IDEAL-2007. Lecture Notes in Computer Science, vol. 4482/2007, 322-329.
  9. Qin, B. , Xia, Y. , Prabhakar, S. , and Tu, Y. , 2009 A Rule-Based Classification Algorithm for Uncertain Data. Data Engineering, ICDE '09. IEEE, 25th International Conference.
  10. Hills, J. , Davis, L. M. , and Bagnall, A. , 2012 Interestingness Measures for Fixed Consequent Rules. Intelligent Data Engineering and Automated Learning, IDEAL-2012. Lecture Notes in Computer Science, vol. 7435/2012, 68-75.
  11. Ding, J. , and Yang, S. , 2012 Classification Rules Mining Model with Genetic Algorithm in Cloud Computing. International Journal of Computer Applications , vol. 48– No. 18, 0975–888.
  12. Noda, E. , Freitas, A. A. , and Lopes, H. S. 1999 Discovering interesting prediction rule with a genetic algorithm. In Proc. of the 1999 Congress on Evolutionary Computation, vol. 2.
  13. Yang, L. , Widyantoro, D. H. , Ioerger, T. , and Yen, J. , 2001 An Entropy-based Adaptive Genetic Algorithm for Learning Classification Rules. In Proc. of the 2001 Congress on Evolutionary Computation, 790–796.
  14. Vashishtha, J. , Kumar, D. , Ratnoo, S. , and Kundu, K. , 2011 Mining Comprehensible and Interesting Rules: A Genetic Algorithm Approach. International Journal of Computer Applications , vol. 31, No. 1, 0975 – 8887.
  15. G¨undogan, K. K. , Alata_s, B. , Karc, A. , Tatar, Y. , 2002 Comprehensible Classification Rule Mining With Two-Level Genetic Algorithm. 2nd FAE International Symposium, TRNC, 373-377.
  16. Fidelis, M. V. , Lopes, H. S. , and Freitas, A. A. , 2000 Discovering Comprehensible Classification Rules with a Genetic Algorithm. In Proc. of Congress on Evolutionary Computation.
  17. Koray, K. , ALATAS, B. , and KARCI, A. , 2004 Mining Classification Rules by Using Genetic Algorithms with Non-random Initial Population and Uniform Operator. Turk J Elec Engin, vol. 12, No. 1.
  18. Dehuri S. , and Mall R. , 2006 Predictive and comprehensible rule discovery using a multi-objective genetic algorithm. Knowledge-Based Systems, vol. 19, 413–421.
  19. 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, 477–487.
  20. Karc, A. , and Arslan, A. , 2002 Uniform population in genetic algorithms. I. ¨U. Journal of Electrical & Electronics, vol. 2, 495-504.
  21. UCI Repository of Machine Learning databases. 1994 http://www. ics. uci. edu/~mlearn/MLRepository. html. Irvine, CA: University of California, Department of Information and Computer Science.
  22. Bilal A. , and Erhan A. , 2009 Multi-objective rule mining using a chaotic particle swarm optimization algorithm. Int. Journal of Knowledge-Based Systems, vol. 22, 455–460
Index Terms

Computer Science
Information Sciences

Keywords

Classification rules Genetic algorithm Significant rule.

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