Comparing different stopping criteria for fuzzy decision tree induction through IDFID3

Iranian Journal of Fuzzy Systems

Volumn 11, Issue 1, 2014, Pages 27-48

  Zeinalkhani, Mohsen ^a   Eftekhari, Mahdi ^a  

^a SHAHID BAHONAR UNIVERSITY OF KERMAN   (Iran)

Author keywords

Classification; Fuzzy decision tree; Fuzzy inference systems; Stopping criteria

Indexed keywords

EID: 84901848404     PISSN: 17350654     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (34)

1. J. Alcal-Fdez, A. Fernandez, J. Luengo, J. Derrac, S. Garca, L. Snchez and F. Herrera, KEEL data-mining software tool: data set repository, integration of algorithms and experimental analysis framework, Journal of Multiple-Valued Logic and Soft Computing, 17 (2011), 255-287.
2. J. Alcal-Fdez, L. Snchez, S. Garca, M. del Jesus, S. Ventura, J. Garrell, J. Otero, C. Romero, J. Bacardit, V. Rivas, J. Fernndez and F. Herrera, KEEL: a software tool to assess evolu-tionary algorithms for data mining problems, Soft Computing, 13(3) (2009), 307-318.
3. L. Bartczuk and D. Rutkowska, Type-2 fuzzy decision trees, In: Proceedings of the 9th Inter-national Conference on Artifficial Intelligence and Soft Computing, Springer-Verlag, Zakopane, Poland, (2008), 197-206.
4. R. B. Bhatt and M. Gopal, Neuro-fuzzy decision trees, International Journal of Neural Systems, 16(1) (2006), 63-78.
5. B. Chandra, P. Paul Varghese, Fuzzy sliq decision tree algorithm, IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 38 (2008), 1294-1301.
6. B. Chandra and P. Paul Varghese, Fuzzifying gini index based decision trees, Expert Systems with Applications, 36(4) (2009), 8549-8559.
7. P. C. Chang, C. Y. Fan and W. Y. Dzan, A CBR-based fuzzy decision tree approach for database classification, Expert Systems with Applications, 37(1) (2010), 214-225.
8. Y. L. Chen, T. Wang, B. S. Wang and Z. J. Li, A survey of fuzzy decision tree classiffer, Fuzzy Information and Engineering, 1(2) (2009), 149-159.
9. M. E. Cintra, M. C. Monard and H. A. Camargo, Evaluation of the pruning impact on fuzzy C4. 5, In: Anais Congresso Brasileiro de Sistemas Fuzzy, (2010), 257-264.
10. J. Demar, Statistical comparisons of classiffers over multiple data sets, Journal of Machine Learning Research, 7 (2006), 1-30.
11. A. Frank and A. Asuncion, UCI machine learning repository, http://archive. ics. uci. edu/ml, Irvine, 2010.
12. S. Garcia and F. Herrera, An extension on "statistical comparisons of classiffers over multiple data sets" for all pairwise comparisons, Journal of Machine Learning Research, 9 (2008), 2677-2694.
13. J. S. R. Jang, C. T. Sun and E. Mizutani, Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence, Prentice Hall, London, 1997.
14. C. Z. Janikow, Fuzzy decision trees: issues and methods, IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, 28(1) (1998), 1-14.
15. R. Jensen and Q. Shen, Fuzzy-rough feature significance for fuzzy decision trees, In: Pro-ceedings of the 2005 UK Workshop on Computational Intelligence, (2005), 89-96.
16. U. Khan, H. Shin, J. Choi and M. Kim, wFDT-weighted fuzzy decision trees for prognosis of breast cancer survivability, In: The Australasian Data Mining Conference, (2008), 141-152.
17. R. E. Korf, Depth-first iterative-deepening: an optimal admissible tree search, In: G. R. Peter, ed., Expert Systems, IEEE Computer Society Press, (1990), 380-389.
18. D. McNeill and P. Freiberger, Fuzzy logic, Simon & Schuster, New York, 1994.
19. T. M. Mitchell, Machine learning, McGraw-Hill, New York, 1997.
20. W. Pedrycz and Z. A. Sosnowski, Designing decision trees with the use of fuzzy granulation, IEEE Transactions on Systems, Man and Cybernetics-Part A: Systems and Humans, 30(2) (2000), 151-159.
21. W. Pedrycz and Z. A. Sosnowski, Genetically optimized fuzzy decision trees, IEEE Transactions on Systems, Man and Cybernetics-Part B: Cybernetics, 35(3) (2005), 633-641.
22. P. Pulkkinen and H. Koivisto, Fuzzy classiffer identification using decision tree and multi-objective evolutionary algorithms, International Journal of Approximate Reasoning, 48(2) (2008), 526-543.
23. J. R. Quinlan, C4. 5: programs for machine learning, Morgan Kaufmann Publishers, San Francisco, 1993.
24. L. Rokach and O. Maimon, Top-down induction of decision trees classiffers-a survey, IEEE Transactions on Systems, Man and Cybernetics-Part C: Applications and Reviews, 35(4) (2005), 476-487.
25. L. Rokach and O. Maimon, Data mining with decision trees: theroy and applications, World Scientific, Singapore, 2008.
26. J. Sanz, A. Fernandez, H. Bustince and F. Herrera, IIVFDT: ignorance functions based interval-valued fuzzy decision tree with genetic tuning, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, in press, 2012.
27. M. Umano, H. Okamoto, I. Hatono, H. Tamura, F. Kawachi, S. Umedzu and J. Kinoshita, Fuzzy decision trees by fuzzy ID3 algorithm and its application to diagnosis systems, In: Proceedings of Third IEEE Conference on Fuzzy Systems, (1994), 2113-2118.
28. T. Wang, Z. Li, Y. Yan and H. Chen, A survey of fuzzy decision tree classiffer methodology, In: B. Y. Cao, ed., Fuzzy Information and Engineering, Springer, Berlin/Heidelberg, (2007), 959-968.
29. X. Wang and C. Borgelt, Information measures in fuzzy decision trees, In: Proceedings of 13th IEEE International Conference on Fuzzy Systems, (2004), 85-90.
30. X. Wang, B. Chen, G. Qian and F. Ye, On the optimization of fuzzy decision trees, Fuzzy Sets and Systems, 112(1) (2000), 117-125.
31. X. Wang and H. Jiarong, On the handling of fuzziness for continuous-valued attributes in decision tree generation, Fuzzy Sets and Systems, 99(3) (1998), 283-290.
32. Y. Yuan and M. J. Shaw, Induction of fuzzy decision trees, Fuzzy Sets and Systems, 69(2) (1995), 125-139.
33. M. Zeinalkhani and M. Eftekhari, A new measure for comparing stopping criteria of fuzzy decision tree, In: International Conference on Computer and Knowledge Engineering, Mash-had, (2011), 120-123.
34. H. Zhang and B. H. Singer, Recursive partitioning and applications, 2nd ed., Springer, Lon-don, 2010.