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Performance Study on Rule-based Classification Techniques across Multiple Database Relations

by M. Thangaraj, C. R. Vijayalakshmi and
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International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 5 - Number 4
Year of Publication: 2013
Authors: M. Thangaraj, C. R. Vijayalakshmi and
10.5120/ijais12-450608

M. Thangaraj, C. R. Vijayalakshmi and . Performance Study on Rule-based Classification Techniques across Multiple Database Relations. International Journal of Applied Information Systems. 5, 4 ( March 2013), 1-7. DOI=10.5120/ijais12-450608

@article{ 10.5120/ijais12-450608,
author = { M. Thangaraj, C. R. Vijayalakshmi and },
title = { Performance Study on Rule-based Classification Techniques across Multiple Database Relations },
journal = { International Journal of Applied Information Systems },
issue_date = { March 2013 },
volume = { 5 },
number = { 4 },
month = { March },
year = { 2013 },
issn = { 2249-0868 },
pages = { 1-7 },
numpages = {9},
url = { https://www.ijais.org/archives/volume5/number4/432-0608/ },
doi = { 10.5120/ijais12-450608 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T17:58:21.705215+05:30
%A M. Thangaraj
%A C. R. Vijayalakshmi and
%T Performance Study on Rule-based Classification Techniques across Multiple Database Relations
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 5
%N 4
%P 1-7
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Classification is an important task in data mining and machine learning which has been studied extensively and has a wide range of applications. There are many classification problem occurs and need to be solved. There are different types of classification algorithms like tree-based, rule-based etc, are widely used. In this paper, a performance comparison of different rule-based classifiers across multiple database relations is presented. Empirical study on both real world and synthetic databases shows their efficiency and accuracy.

References
  1. Catlett, 1992. Ripple-Down-Rules as a mediating representation in interactive induction. In Proceedings of the Second Japanese Knowledge Acquisition for Knowledge-Based Systems Workshop, Kobe, Japan.
  2. Compton, P. , Edwards, G. , et al. , Ripple down rules: turning knowledge acquisition into knowledge maintenance, Artificial Intelligence in Medicine 4: 47-59.
  3. Compton, P. , Edwards, G. , Kang, B. , Lazarus, L. , Malor, R. 1991. Ripple down rules: Possibilities and limitations.
  4. Emde ,W. , Wettschereck D. Relational Instance –Based Learning. In L. Saitta, editor, Proceedings 13th International Conference on Machine Learning, pages 122-130. Morgan Kaufmann, 1996.
  5. Frank, E. , Witten, I. , J. 1998. Generating Accurate Rule Sets without Global Optimization, Machine Learning: Proceedings of the Fifteenth International Conference, pp. 144-151, Madison, Wisconsin, Morgan Kaufmann, San Francisco.
  6. Friedman N. , Getoor L. , Koller D. , and Pfeffer A. 1999. Learning Probabilistic Relational Models. In Sixteenth International Joint Conference on Artificial Intelligence (IJCAI).
  7. Furnkranz, J. , Widmer, G. 1994. Incremental Reduced Error Pruning. In Machine Learning: Proceedings of the 11th Annual Conference, New Brunswick, New Jersey, Morgan Kaufmann.
  8. Gaines, B. R. , Compton, P. J. 1992. Induction of Ripple Down Rules, AI Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, Hobart, Australia, World Scientific, Singapore.
  9. Gaines, B. R. , Paul Compton, J. 1995. Induction of Ripple-Down Rules Applied to Modeling Large Databases, Intell. Inf. Syst. 5(3):211-228.
  10. Gupta, P. , Toshniwal, D. , J. 2011. Performance Comparison of Rule Based Classification Algorithms, Computer Science & Informatics, Volume-I, Issue-II.
  11. Han, J. , Kamber, M. 2001. Data Mining: Concepts and Techniques", Morgan Kaufmann.
  12. Ibrahim, F. , Abu Osman, N. A. , and Usman, Kadri, N. A. 2007. Comparison of Different Classification Techniques Using WEKA for Breast Cancer, (Eds. ): Biomed 06, IFMBE Proceedings 15, pp. 520-523, Springer-Verlag Berlin Heidelberg.
  13. Kramer, S. , N. Lavrac and P. Flach. 2001. Propositionalization approaches to relational data mining. In S. Dzeroski and N. Lavrac, eds. Relational Data Mining. Pp 262-291, Springer-Verlag.
  14. Lakshmi Devasena, J. 2011. Effectiveness Evaluation of Rule Based Classifiers for the Classification of Iris Data Set, Bonfring International Journal of Man Machine Interface, Vol. 1, Special Issue, December.
  15. Lavrac N. and Dzeroski S. 1994. Inductive Programming: Techniques and Applications. Ellis Hoodwood.
  16. Lehr, T. , Yuan, J. , and Zuemer, D. , Jeyadev, S. J. 2011. Rule based classifier for the analysis of gene-gene and gene-environment interactions in genetic association studies, BioData Mining 4:4.
  17. Milan Kumari, Sunila Godara, J. 2011. Comparative Study of Data Mining Classification Methods in Cardiovascular Disease Prediction, Computer Science and Technology, Vol. 2, Issue 2, June.
  18. Neville J. , Jensen D. , Friedland L. , and Hay M. , 2003. Learning Relational Probability Trees. Technical Report 02-25, Dept. of Computer Science, University of Massachusetts Amherst, 2002, Revised version February.
  19. Quinlan, R. , 1993. C4. 5: Programs for Machine Learning San Mateo, CA: Morgan Kaufmann Publishers.
  20. Shelly Gupta, Kumar, D. , J. 2011. Data Mining Classification techniques applied for breast cancer diagnosis and prognosis, Indian Journal of Computer Science and Engineering, Vol. 2 No. 2 Apr-May.
  21. William, Cohen, 1995. Fast Effective Rule Induction, In: Twelfth International Conference on Machine Learning, 115-123.
  22. Yang, J, . Tiyyagura, et al, 2001. Feature Subset selection for Rule Induction Using RIPPER.
  23. Yin, X. Han, Yu PS, J. 2006. Efficient Classification across Multiple Database Relations: A CrossMine Approach, IEEE Transactions on Knowledge and Data Engineering, Vol 16, No. 6.
  24. Yin, X. , Han, J. , Yang, J. 2003. Efficient Multi-relational Classification by Tuple ID Propagation, In Proceedings of KDD workshop on MRDM.
  25. Yin, X. , Han, J. , Yu PS, 2004. CrossMine: Efficient Classification across Multiple Database Relations. In Proceedings of 20th Int. Conf. on Data Engineering (ICDE'04).
Index Terms

Computer Science
Information Sciences

Keywords

Multi-relational classification RIPPER RIDOR PART Tuple ID propagation

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