A new classification method for breast cancer diagnosis: Feature Selection Artificial Immune Recognition System (FS-AIRS)

Lecture Notes in Computer Science

Volumn 3611, Issue PART II, 2005, Pages 830-838

  Polat, Kemal ^a   Sahan, Seral ^a   Kodaz, Halife ^a   Günes, Salih ^a  

^a SELCUK UNIVERSITY   (Turkey)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTER SCIENCE; DATABASE SYSTEMS; IMMUNOLOGY;

BREAST CANCER; IMMUNE RECOGNITION SYSTEMS;

TUMORS;

EID: 26844460223     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11539117_117     Document Type: Conference Paper

References (19)

1. Du, X.L., Key, C.R., Osborne, C., Mahnken, J.D., Goodwin, J.S.: Discrepancy Between Consensus Recommendations and Actual Community Use of Adjuvant Chemotherapy in Women with Breast Cancer, Annals of Internal Medicine, Vol. 138, (2003) 90-97
2. Quinlan, J.R.: Improved use of continuous attributes in C4.5, Journal of Artificial Intelligence Research, Vol. 4, (1996) 77-90
3. Hamilton, H.J., Shan, N. and Cercone, N.: RIAC: A Rule Induction Algorithm Based on Approximate Classification, Tech. Rep. CS 96-06, Regina University (1996)
4. Ster, B. and Dobnikar, A.: Neural Networks in Medical Diagnosis: Comparison With Other Methods, Proceedings of the International Conference EANN '96, (1996) 427-430
5. Bennett, K.P. and Blue, J.A.: A Support Vector Machine Approach to Decision Trees, Rensselaer Polytechnic Institute, Math Report, (1997) 97-100
6. Nauck, D. and Kruse, R.: Obtaining Interpretable Fuzzy Classification Rules From Medical Data, Artificial Intelligence in Medicine, Vol. 16, (1999) 149-169
7. Pena-Reyes, A.C., Sipper, M.: A Fuzzy-Genetic Approach to Breast Cancer Diagnosis, Artificial Intelligence in Medicine. Vol. 17, (1999) 131-155
8. Setiono, R.: Generating Concise and Accurate Classification Rules for Breast Cancer Diagnosis, Artificial Intelligence in Medicine. Vol. 18 (2000) 205-219
9. Goodman, E.D., Boggess, C.L., Watkins, A.: Artificial Immune System Classification of Multiple-Class Problems, In Intelligent Engineering Systems through Artificial Neural Networks: Smart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Complex Systems and Artificial Life, Vol. 12, (2002) 179-184
10. Abonyi, J., Szeifert, F.: Supervised Fuzzy Clustering for The Identification of Fuzzy Classifiers, Pattern Recognition Letters. Vol. 24, (2003) 2195-2207
11. Blake, C.L., and Merz, C.J.: UJI Repository of Machine Learning Databases, ftp://ftp.ics. uci.edu/pub/machine-learning-databases (last accessed: 7 April 2005)
12. Nasaroui, O.F., Gonzalez, F., Dasgupta, D.: The Fuzzy Artificial Immune System: Motivation, Basic Concepts, and Application to Clustering and Web Profiling, International Joint Conference on Fuzzy Systems, (2002) 711-717
13. De Castro, L.N., Timmis, J.: Artificial Immune Systems: A New Computational Intelligence Approach, Springer-Verlag Press (2002)
14. Knight, T., and Timmis, J.: Assessing The Performance of The Resource Limited Artificial Immune System AINE, Computing Laboratory, University of Kent at Canterbury, Technical Report (2001)
15. Watkins, A., and Timmis, J.: Artificial Immune Recognition System (AIRS): Revisions and Refinements, Proc. Int. Conf. on Artificial Immune Systems (ICARIS), (2002) 99-106
16. Dasgupta, D.: Artificial Immune Systems and Their Applications, Springer-Verlag Press, Germany (1999)
17. Carter, J. H.: The Immune System as a Model for Pattern Recognition and Classification, Journal of the American Medical Informatics Assocation, Vol. 7(3), (2000) 28-41
18. Watkins, A.: AIRS: A Resource Limited Artificial Immune Classifier, A Thesis Submitted to the Faculty of Mississippi State University (2001)
19. Sahan, S., Kodaz, H., Güneş, S., Polat, K.: A New Classifier Based on Attribute Weighted Artificial Immune System, Lecture Notes in Computer Science (LNSC 3280), (2004) 11-20