Diagnosis of melanoma using IRIM, a data mining system

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3070, Issue , 2004, Pages 996-1001

  Grzymala Busse, Jerzy W ^a,b   Hamilton, Jay ^a   Hippe, Zdzislaw S ^c  

^a UNIVERSITY OF KANSAS   (United States)

^b INSTITUTE OF COMPUTER SCIENCE   (Poland)

^c UNIVERSITY OF INFORMATION TECHNOLOGY AND MANAGEMENT   (Poland)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; DATA PROCESSING; DIAGNOSIS; LEARNING SYSTEMS; SKIN;

DATA MINING TOOLS; DATA SETS; MELANOMA; SKIN CANCER;

TUMORS;

EID: 9444229393     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-24844-6_155     Document Type: Conference Paper

References (12)

1. Alvarez, A., Brown, F. M., Grzymala-Busse, J. W., and Hippe, Z. S.: Optimization of the ABCD formula used for melanoma diagnosis. Proc. of the IIPWM'2003, Int. Conf. On Intelligent Information Processing and WEB Mining Systems, Zakopane, Poland, June 2-5, 2003, 233-240.
2. Grzymala-Busse, J. W.: LERS - A system for learning from examples based on rough sets. In Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory. Slowinski, R. (ed.), Kluwer Academic Publishers, Dordrecht, Boston, London (1992) 3-18.
3. Grzymala-Busse J. W. and Hippe Z. S.: Postprocessing of rule sets induced from a melanoma data set. Proc. of the COMPSAC 2002, 26th Annual International Conference on Computer Software and Applications, Oxford, England, August 26-29, 2002, 1146-1151.
4. Grzymala-Busse J. W. and Hippe Z. S.: A search for the best data mining method to predict melanoma. Proceedings of the RSCTC 2002, Third International Conference on Rough Sets and Current Trends in Computing, Malvern, PA, October 14-16, 2002, Springer-Verlag, Berlin, Heidelberg, New York (2002) 538-545.
5. Grzymala-Busse, J. W. and Wang A. Y.: Modified algorithms LEM1 and LEM2 for rule induction from data with missing attribute values. Proc. of the Fifth International Workshop on Rough Sets and Soft Computing (RSSC'97) at the Third Joint Conference on Information Sciences (JCIS'97), Research Triangle Park, NC, March 2-5, 1997, 69-72.
6. Hippe, Z. S.: Computer database NEVI on endargement by melanoma. Task Quarterly 4 (1999) 483-488.
7. R. G. Holte. Very simple classification rules perform well on most commonly used datasets. Machine Learning 11 (1993) 63-90.
8. R. S. Michalski, I. Mozetic, J. Hong, N. Lavrac.: The AQ15 Inductive Learning System: An Overview and Experiments. Intelligent System Group, University of Illinois at Urbana-Champaign, ISG 86-20, 1986.
9. Pawlak, Z.: Rough Sets. International Journal of Computer and Information Sciences 11 (1982) 341-356.
10. Quinlan, J. R.: C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, San Mateo, CA (1988).
11. Stefanowski, J.: Algorithms of Decision Rule Induction in Data Mining (in Polish). Poznan University of Technology Press, Poznan, Poland (2001).
12. Stolz, W., Braun-Falco, O., Bilek, P., Landthaler, A. B., Cogneta, A. B.: Color Atlas of Dermatology, Blackwell Science Inc., Cambridge, MA (1993).