On the optimization of T-norm parameters within fuzzy decision trees

IEEE International Conference on Fuzzy Systems

Volumn , Issue , 2007, Pages

  Crockett, Keeley ^a,b   Bandar, Zuhair ^c   Mclean, David ^b  

^a IEEE

^b MANCHESTER METROPOLITAN UNIVERSITY   (United Kingdom)

^c NONE

Author keywords

[No Author keywords available]

Indexed keywords

CONTROL THEORY; DECISION MAKING; DECISION THEORY; DECISION TREES; DIESEL ENGINES; FUZZY CONTROL; FUZZY LOGIC; FUZZY SETS; FUZZY SYSTEMS; GENETIC ALGORITHMS; MEMBERSHIP FUNCTIONS; OPTIMIZATION; SET THEORY; TREES (MATHEMATICS); TUNING;

APPLICATIONS.; COMPARATIVE STUDIES; FUZZY DECISION TREE; FUZZY DECISION TREES; FUZZY MEMBERSHIP FUNCTIONS; FUZZY OPERATORS; FUZZY TREES; INTERNATIONAL CONFERENCES; PARAMETERIZED; PROBLEM DOMAINS; REAL-WORLD DATASETS; SPECIFIC DOMAIN; T-CONORMS; T-NORM OPERATORS;

FUZZY INFERENCE;

EID: 50349087527     PISSN: 10987584     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/FUZZY.2007.4295348     Document Type: Conference Paper

References (29)

1. Bouchon-Meunier, B Mamdani, R. Learning From Imperfect Data, In "Fuzzy Information Engineering A Guided Tour of Applications", Eds. Dubois, D. Prade, H. Yager, R, R, John Wiley and Sons Publishing, pp. 139-148. 1997.
2. Marsala, C. Bouchon-Meunier, B. Ranking Attributes to Build Fuzzy Decision Trees: a Comparative Study of Measures. 2006 IEEE International Conference on Fuzzy Systems, pp8436-8442, 2006.
3. Olaru, C. Wehenkel, A complete fuzzy decision tree technique. Fuzzy sets and systems: 138, pp 221-254, 2003
4. Janikow, C. Fuzzy Decision Trees: Issues and Methods, IEEE Transactions on Systems, Man, and Cybernetics, Vol.28,1, pp. 1-14, 1998
5. Janikow, C. Fuzzy Information Processing NAFIPS '04. Volume 2, 27-30, pp877 - 881, 2004.
6. Umano, M. Okamoto, H. Hatono, I. Tamura, H. "Generation of Fuzzy Decision Trees By Fuzzy ID3 Algorithm and it's Application to Diagnosis by Gas in Oil", Japan-U.S.A Symposium, pp. 1445-1450, 1994
7. Sison,L. Chong, E. "Fuzzy Modeling By Induction and Pruning of Decision Trees", IEEE Symposium on Intelligent Control U.S.A. pp. 166-171, 1994.
8. Zadeh, L. Fuzzy Sets. Information and Control 8, 1965, pp228-353
9. Yager,R. A General Class Of Fuzzy Connectives. Fuzzy Sets and Systems 4, 1980
10. Dubois, D. Prade, H. "A Class Of Fuzzy Measures Based On Triangular Norms", International Journal of General Systems 8, pp. 43-61, 1982.
11. Dombi, J. "A General Class of Fuzzy Operators, the Demorgan Class of Fuzzy Operators and Fuzziness Measures Induced by Fuzzy Operators", Fuzzy Sets and Systems 8, pp. 149-163, 1992.
12. Yager, R. Uncertainty representation using fuzzy measures, IEEE Transaction on Systems, Man and Cybernetics 32, pp13-20, 2002
13. Garmendia, L., Yager, R. R., Yrillas, E. Salvador, E. "On T-norm based measures of specificity," Fuzzy Sets and Systems 133, 237-248, 2003
14. Lee, C. Narayanan, S. Emotion Recognition Using a Data-Driven Fuzzy Inference System, 8th European Conference Speech Communication Technology, Switzerland, 2003.
15. Dubois, D. Prade, H. "On the Use of Aggregation Operations in Information Fusion Process", Fuzzy Sets and Systems: 142, pp 143-161, 2004
16. Chen, M. Linkens, D. Rule-base self-generation and simplification for data-driven fuzzy models, Fuzzy Sets and Systems: 142, pp243-265, 2003
17. Ching-chang, W. Hsuan-Ming, F. Design of Fuzzy Systems with Fewer Rules, Cybernetics and Systems: An International Journal, 34: pp139-160, 2003
18. Yeh, Z. Li, K. A Systematic Approach for Designing Multistage Fuzzy Control Systems, Fuzzy Sets and Systems:143, pp 251-273, 2004
19. Crockett, K., Bandar, Z., O'Shea, J. Mclean, D. On Constructing a Fuzzy Inference Framework using Crisp Decision Trees. Fuzzy Sets and Systems V 157, No 21, pp 2809-2832, 2006
20. Crockett, K. Fowdar, J. Bandar, Z, O'Shea, J. On the Use of Fuzzy Trees for Solving Classification Problems with Numeric Outcomes. 2005 IEEE Conference on Fuzzy Systems, May 2005
21. Cordon, O. Gomide, F. Herrera, F. Hoffmann, F. Magdalena. "Ten years of Genetic Fuzzy Systems: Current Framework and New Trends". Fuzzy Sets and Systems: 141, pp 5 - 31, 2004
22. Quinlan, J.R., Is C5.0 Better Than C4.5. http://www.rulequest.com/see5- comparison.html, 1997
23. Mamdani, E. H. and A. S., "An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller." International Journal of Human Computer Studies 51, pp135-147, 1999
24. Klir, G. Folder, T. Fuzzy Sets, Uncertainty and Information. PH, 1982
25. Mendel, J.M. Fuzzy Logic Systems in Engineering: A Tutorial, Proceedings of IEEE, vol 83(3), pp 345-377, 1995.
26. Goldberg, D. A Meditation on the Application of Genetic Algorithms. liliGal Report 98003 .http://gal4.ge.uiuc.edu/illigal.home.html., 1998
27. Blake, C. L. and C. J. Merz, UCI Repository of machine learning databases. Irvine, CA: Universit of California, Department of Information and Computer Science,1998
28. Attar Software Limited, Newlands Road, Leigh, Lancashire, WN7 4HN, UK
29. Sigillito, V. Original Owners: National Institute of Diabetes, Digestive and Kidney Diseases. Donor: Vincent Sigillito (vgs @aplcen.apl.jhu.edu), The Johns Hopkins University, Laurel, MD 20707.pp. 953-6231., 2002