A constrained-syntax genetic programming system for discovering classification rules: Application to medical data sets

Artificial Intelligence in Medicine

Volumn 30, Issue 1, 2004, Pages 27-48

  Bojarczuk, Celia C ^a   Lopes, Heitor S ^a   Freitas, Alex A ^b   Michalkiewicz, Edson L ^c  

^a UNIVERSIDADE TECNOLÓGICA FEDERAL DO PARANÁ   (Brazil)

^b UNIVERSITY OF KENT   (United Kingdom)

^c Hospital Erasto Gaertner   (Brazil)

Author keywords

Classification rules; Constrained syntax genetic programming; Data mining

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DECISION THEORY; SYNTACTICS; TUMORS;

BREAST CANCER; GENETIC PROGRAMMING;

MEDICINE;

ADRENAL TUMOR; ALGORITHM; BREAST CANCER; COMPUTER PROGRAM; COMPUTER SYSTEM; DATA ANALYSIS; DECISION THEORY; DERMATOLOGY; DISEASE CLASSIFICATION; INFORMATION PROCESSING; INTERMETHOD COMPARISON; MEDICAL INFORMATION SYSTEM; PREDICTION; PRIORITY JOURNAL; REVIEW; SURVIVAL RATE; THORAX PAIN;

EID: 0347479357     PISSN: 09333657     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.artmed.2003.06.001     Document Type: Article

References (31)

1. Banzhaf W, Nordin P, Keller RE, Francone FD. Genetic programming - an introduction: on the automatic evolution of computer programs and its applications. San Francisco; Morgan Kaufmann; 1998.
2. Bojarczuk C.C., Lopes H.S., Freitas A.A. Genetic programming for knowledge discovery in chest pain diagnosis. IEEE Eng. Med. Biol. Mag. 19:2000;38-44.
3. Bojarczuk CC, Lopes HS, Freitas AA. An innovative application of a constrained-syntax genetic programming system to the problem of predicting survival of patients. In: Proceedings of the 6th European Conference on Genetic Programming (EuroGP-2003), LNCS 2610. Heidelberg: Springer-Verlag; 2003. p. 11-21.
4. Clack C, Yu T. PolyGP: a polymorphic genetic programming system in Haskell. In: Proceedings of the 3rd Annual Conference on Genetic Programming 1998. San Francisco: Morgan Kaufmann; 1998. p. 416-21.
5. Dhar V., Chou D., Provost F. Discovering interesting patterns for investment decision making with GLOWER - a genetic learner overlaid with entropy reduction. Data Mining Know. Discovery J. 4:2000;251-280.
6. Fayyad UM, Piatetsky-Shapiro G, Smyth P. From data mining to knowledge discovery: an overview. In: Fayyad UM, et al., editors. Advances in knowledge discovery and data mining. Menlo Park: AAAI/MIT; 1996. p. 1-34.
7. Freeman JJ. A linear representation for GP using context free grammars. In: Proceedings of the 3rd Annual Conference on Genetic Programming. San Francisco: Morgan Kaufmann; 1998. p. 72-7.
8. Freitas A.A. Understanding the crucial role of attribute interaction in data mining. Artif. Intell. Rev. 16:2001;177-199.
9. Freitas AA. A survey of evolutionary algorithms for data mining and knowledge discovery. In: Ghosh A, Tsutsui S, editors. Advances in evolutionary computation. Berlin: Springer-Verlag; 2002. p. 819-45.
10. Freitas AA. Data mining and knowledge discovery with evolutionary algorithms. Berlin: Springer-Verlag; 2002.
11. Freitas AA. Evolutionary algorithms. In: Zytkow J, Klosgen W, editors. Handbook of data mining and knowledge discovery. Oxford: Oxford University Press; 2002. p. 698-706.
12. Greene D.P., Smith S.F. Competition-based induction of decision models from examples. Machine Learn. 13:1993;229-257.
13. Gruau F. Cellullar encoding of genetic neural networks. Technical Report 92-21. Ecole Normale Superieure de Lyon (France): Laboratorie de l'Informatique du Parallelisme; 1992.
14. Hand DJ. Construction and assessment of classification rules. Chicester: Wiley; 1997.
15. Janikow CZ, Deweese S. Processing constraints in genetic programming with CGP2.1. In: Proceedings of the 3rd Annual Conference on Genetic Programming. San Francisco: Morgan Kaufmann; 1998. p. 73-180.
16. Kishore J.K., Patnaik L.M., Mani V., Agrawal V.K. Application of genetic programming for multicategory pattern classification. IEEE Trans. Evol. Comput. 4(3):2000;242-258.
17. Koza JR. Genetic programming: on the programming of computers by means of natural selection. Cambridge: MIT Press; 1992.
18. Lopes H.S., Coutinho M.S., Heinisch R., Barreto J.M., Lima W.C. A knowledge-based system for decision support in chest pain diagnosis. Med. Biol. Eng. Comput. 35(Part I):1997;514.
19. Montana D.J. Strongly typed genetic programming. Evol. Comput. 3:1995;199-230.
20. Michie D, Spiegelhalter DJ, Taylor CC. Machine learning, neural and statistical classification. New York: Ellis Horwood; 1994.
21. Mitchell TM. Machine learning. New York: McGraw-Hill; 1997.
22. Ngan PS, Wong ML, Leung KS, Cheng JCK. Using grammar-based genetic programming for data mining of medical knowledge. In: Proceedings of the 3rd Annual Conference on Genetic Programming. San Francisco: Morgan Kaufmann; 1998. p. 254-9.
23. Papagelis A, Kalles D. Breeding decision trees using evolutionary techniques. In: Proceedings of the 18th International Conference on Machine Learning (ICML-2001). San Mateo: Morgan Kaufmann; 2001. p. 393-400.
24. Quinlan JR. C4.5: programs for machine learning. San Mateo (CA): Morgan Kaufmann; 1993.
25. Rao RB, Gordon D, Spears W. For every generalization action, is there really an equal and opposite reaction? Analysis of the conservation law for generalization performance. In: Proceedings of 12th International Conference on Machine Learning. San Mateo (CA): Morgan Kaufmann; 1995. p. 471-9.
26. Schaffer C. A conservation law for generalization performance. In: Proceedings of 12th International Conference on Machine Learning. San Mateo (CA): Morgan Kaufmann; 1994. p. 259-65.
27. Teller A, Veloso M. PADO: a new learning architecture for object recognition. In: Ikeuchi K, Veloso M, editors. Symbolic visual learning. Oxford: Oxford University Press; 1996. p. 81-116.
28. Weiss SM, Kulikowski CA. Computer systems that learn. San Mateo: Morgan Kaufmann; 1991.
29. Whigham PA. Grammatically-based genetic programming. In: Rosca JP, editor. In: Proceedings of the Workshop on Genetic Programming: From Theory to Real-World Applications. Tahoe City (CA): Morgan Kaufmann; 1995. p. 33-41.
30. Witten IH, Frank E. Data mining: practical machine learning tools and techniques with Java implementations. San Mateo: Morgan Kaufmann; 2000.
31. Wong ML, Leung KS. Data mining using grammar based genetic programming and applications. In: Genetic programming, vol. 3. The Netherlands: Kluwer Academic Publishers; 2000.