Discretization techniques and genetic algorithm for learning the classification method PROAFTN

8th International Conference on Machine Learning and Applications, ICMLA 2009

Volumn , Issue , 2009, Pages 685-688

  Al Obeidat, Feras ^a   Belacel, Nabil ^b   Mahanti, Prabhat ^a   Carretero, Juan A ^a  

^a UNIVERSITY OF NEW BRUNSWICK   (Canada)

^b NATIONAL RESEARCH COUNCIL CANADA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATIC METHOD; CLASSIFICATION ,; CLASSIFICATION METHODS; CLASSIFICATION MODELS; DISCRETIZATIONS; MINIMUM CLASSIFICATION ERROR; MULTI-CRITERIA CLASSIFICATION;

AIRFOILS; CLASSIFICATION (OF INFORMATION); DATA MINING; LEARNING SYSTEMS;

LEARNING ALGORITHMS;

EID: 77950822772     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2009.37     Document Type: Conference Paper

References (16)

1. E. Alpaydin. Introduction to machine learning (adaptive computation and machine learning). MIT Press, 2004.
2. A. Asuncion and D. Newman. UCI machine learning repository, 2007.
3. N. Belacel. Multicriteria assignment method proaftn: methodology and medical application. European Journal of Operational Research, 125(1):175-183, 2000.
4. N. Belacel andM. Boulassel. Multicriteria fuzzy assignment method: A useful tool to assist medical diagnosis. Artificial Intelligence in Medicine, 21(1-3):201-207, 2001.
5. N. Belacel, H. Raval, and A. Punnen. Learning multicriteria fuzzy classification method proaftn from data. Computers and Operations Research, 34(7):1885-1898, 2007.
6. N. Belacel, P. Vincke, M. Scheiff, and M. Boulassel. Acute leukemia diagnosis aid using multicriteria fuzzy assignment methodology. Computer Methods and Programs in Biomedicine, 64(2):145-151, 2001.
7. N. Belacel, Q. Wang, and R. Richard. Web-integration proaftn methodology for acute leukemia diagnosis. TELEMEDICINE AND e-HEALTH, 11(6):1885-1898, 2005.
8. L. Dias and V. Mousseau. Inferring electres veto-related parameters from outranking examples. European Journal of Operational Research, 170(1):172-191, 2006.
9. F. Divina and E. Marchiori. Handling continuous attributes in an evolutionary inductive learner. in IEEE Transactions on Evolutionary Computation, 9(1):31-43, 2005.
10. D. Goldberg. Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional, 1989.
11. J. Lger and J.-M. Martel. A multi-criteria assignment procedure for a nominal sorting problematic. European Journal of Operational Research, 138:349-364, 2002.
12. C. R. Reeves and J. E. Rowe. Genetic Algorithms: Principles and Perspectives. A Guide to GA Theory. Kluwer Academic Publishers, 2002.
13. B. Roy. Multicriteria methodology for decision aiding. Kluwer Academic, 1996.
14. P. Smyth, D. Pregibon, and C. Faloutsos. Data-driven evolution of data mining algorithms. Comm. ACM, 45(8):33-37, 2002.
15. H. Witten. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Series in Data Management Systems, 2005.
16. C. Zopounidis and M. Doumpos. Multicriteria classification and sorting methods: A literature review. European Journal of Operational Research, 138(2):229-246, 2002. (Pubitemid 34065095)