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Mining breast cancer data with XCS
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Genetic And Evolutionary Computation Conference archive
Proceedings of the 9th annual conference on Genetic and evolutionary computation table of contents
London, England
SESSION: Real-world applications: papers table of contents
Pages: 2066 - 2073  
Year of Publication: 2007
ISBN:978-1-59593-697-4
Authors
Faten Kharbat  Zarqa Private University
Larry Bull  University of the West of England
Mohammed Odeh  University of the West of England
Sponsors
SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 3,   Downloads (12 Months): 54,   Citation Count: 3
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ABSTRACT

In this paper, we describe the use of a modern learning classifier system to a data mining task. In particular, in collaboration with a medical specialist, we apply XCS to a primary breast cancer data set. Our results indicate more effective knowledge discovery than with C4.5.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Kharbat, F. (2006) Learning Classifier Systems for Knowledge Discovery in Breast Cancer. PhD Dissertation. University of the West of England, U.K.
 
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Kharbat, F., Bull, L. & Odeh, M. (2005) Revisiting Genetic Selection in the XCS Learning Classifier System. In Proceedings of the IEEE Congress on Evolutionary Computation. IEEE, pp2061--2068.
 
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Collaborative Colleagues:
Faten Kharbat: colleagues
Larry Bull: colleagues
Mohammed Odeh: colleagues