Rough sets method for SVM data preprocessing

2004 IEEE Conference on Cybernetics and Intelligent Systems

Volumn , Issue , 2004, Pages 1038-1041

  Li, Ye ^a   Cai, Yun Ze ^a   Li, Yuan Gui ^a   Xu, Xiao Ming ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Classification; Dependency; Reduction; Rough sets; SVM

Indexed keywords

APPROXIMATION THEORY; BENCHMARKING; DECISION MAKING; MATHEMATICAL MODELS; NEURAL NETWORKS; PROBLEM SOLVING; ROUGH SET THEORY;

CLASSIFICATION; DEPENDENCY; ROUGH SETS; SUPPORT VECTOR MECHANICS (SVM);

DATA PROCESSING;

EID: 11244273139     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. V. Vapnik, The Nature of Statistical Learning Theory, New York: Springer-Verlag, 1995
2. Pawlak Z., "Rough Sets Theory and its Applications to Data Analysis", Cybernetics & Systems [J], An Int'l.J., Vo129,1998: 661-688
3. Aijun An et al, "Applying Knowledge Discovery to Predict Water-Supply Consumption", IEEE Expert [J], July/August 1997: 72-78
4. Pawlak Z., Slowinski R., "Rough Set Approach to Multiattribute Decision Analysis", Invited Review [J], European Journal of Operational Research, 1994, 72:443-459
5. Jeonek J., Krawiec K., Slowinski R., "Rough Set Reduction of Attributes and Their Domains for Neural Networks", Computer Intelligence, 1995, 11(2): 339-347
6. Pawlak Z., "Rough sets", International Journal of Computer and Information Science, Vol.11, No.5, 1982: 341-356
7. Polkowski L. Skowron A., "Rough Mereology: A New Paradigm for Approximate Reasoning", International Journal of Approximate Reasoning, Volume: 15, Issue: 4, November, 1996, pp. 333-365
8. UCI KDD Archive, http://kdd.ics.uci.edu/
9. http://www.csie.ntu.edu.tw/~cjlin/libsvm
10. http://rosetta.lcb.uu.se/general/
11. Son H. Nguyen, "Some efficient algorithms for rough set methods", in Proceedings of the Conference of Information Processing and Management of Uncertainty in Knowledge-Based Systems IPMU'96, July 1996, Granada, Spain, pp. 1451-14
12. Cortes C. and V. Vapnik, "Support-Vector Network", Machine Learning, 1995, 20:273-297