A rough set-based multiple criteria linear programming approach for the medical diagnosis and prognosis

Expert Systems with Applications

Volumn 36, Issue 5, 2009, Pages 8932-8937

  Zhang, Zhiwang ^a,b   Shi, Yong ^b,c   Gao, Guangxia ^d  

^a UNIVERSITY OF CHINESE ACADEMY OF SCIENCES   (China)

^b INSTITUTE OF GEOLOGY AND GEOPHYSICS   (China)

^c UNIVERSITY OF NEBRASKA AT OMAHA   (United States)

^d SHANDONG INSTITUTE OF BUSINESS AND TECHNOLOGY   (China)

Author keywords

Classification; Data mining; Multiple criteria linear programming; Rough set

Indexed keywords

DATA MINING; DECISION TREES; DIAGNOSIS; DYNAMIC PROGRAMMING; FUZZY SETS; INFORMATION MANAGEMENT; LINEAR PROGRAMMING; LINEARIZATION; MEDICAL COMPUTING; MULTILAYER NEURAL NETWORKS; OPTIMIZATION; PARTICLE SIZE ANALYSIS; PROGRAM DIAGNOSTICS; RAPID SOLIDIFICATION; SUPPORT VECTOR MACHINES;

BASIC THEORIES; BUSINESS DECISIONS; CLASSIFICATION; CLASSIFICATION METHODS; FUZZY SET; HIDDEN INFORMATIONS; MEDICAL DIAGNOSIS; MULTIPLE CRITERIA LINEAR PROGRAMMING; ROUGH SET; WINDOWS SYSTEMS;

ROUGH SET THEORY;

EID: 60849139385     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2008.11.007     Document Type: Article

References (19)

1. Bhatt R.B., and Gopal M. On fuzzy-rough sets approach to feature selection. Pattern Recognition Letters 26 (2005) 965-975
2. Dantzig G.B., and Thapa M.N. Linear programming volume I: Introduction (1997), Springer
3. Freed N., and Glover F. Simple but powerful goal programming models for discriminant problems. European Journal of Operational Research 7 (1981) 44-60
4. Han J., and Kamber M. Data mining: Concepts and techniques (2001), Morgan Kaufman Publishers, San Francisco, California
5. Komorowski J., Polkowski L., and Skowron A. Rough sets: A tutorial (1998), Springer
6. Kou G., Xiantao L., Peng Y., Shi Y., Morganwise, and Xu W. Multiple criteria linear programming approach to data mining, models, algorithm designs and software development. Optimization Methods and Software 18 4 (2003) 453-473 August
7. Lin T.Y., and Liau C.-J. Computing, Granular, & Sets, Rough, An incremental development. In: Maimon O., and Rokach L. (Eds). Data mining and knowledge discovery handbook (2005), Springer, Israel 535-558
8. Mangasarian O.L., Street W.N., and Wolberg W.H. Breast cancer diagnosis and prognosis via linear programming. Operations Research 43 4 (1995) 570-577 July-August
9. Mehmed Kantardzic. Data mining: Concepts, models, methods, and algorithms (2003), John Wiley & Sons pp. 64-66
10. Pardalos P.M., and Hearn D. Multicriteria decision aid classification methods, applied optimization Vol. 73 (2005), Kluwer Academic Publishers pp. 39-66
11. Pawlak Z. Rough sets. International Journal of Computation and Information Sciences 11 (1982) 341-356
12. Peters J.F., and Skowron A. Transactions on rough sets I (2004), Springer
13. http://www.sas.com/.
14. Shi Y., Wise M., Luo M., and Lin Y. Data mining in credit card portfolio management: A multiple criteria decision making approach. Advance in Multiple Criteria Decision Making in the New Millennium (2001), Springer, Berlin
15. Swiniarski R.W., and Skowron A. Rough set methods in feature selection and recognition. Pattern Recognition Letters 24 (2003) 833-849
16. Yosukizu, Hirotaki N., and Tanino T. Theory of multiobjective optimization, mathematics science and engineering Vol. 176 (1985), Academic Press, Japan
17. Zhai L.-Y., Khoo L.-P., and Fok S.-C. Knowledge acquisition and uncertainty in fault diagonsis. In: Triantaphyllou E., and Felici G. (Eds). Data mining and knowledge discovery approaches based on rule induction (2006), Springer, USA 359-394
18. Zhao Y., Yao Y., and Luo F. Data analysis based on discernibility and indiscernibility, Information Sciences Vol. 177 (2006), Elsevier November
19. http://archive.ics.uci.edu/ml/.