Data mining using genetic programming: The implications of parsimony on generalization error

Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999

Volumn 2, Issue , 1999, Pages 1330-1337

  Cavaretta, Michael J ^a   Chellapilla, Kumar ^b  

^a FORD RESEARCH LABORATORY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; GENETIC PROGRAMMING;

COMMON DATUM; COMPLEX MODEL; GENERALIZATION ERROR; HIGHLY ACCURATE; TRAINING ERRORS;

GENETIC ALGORITHMS;

EID: 84901439894     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.1999.782602     Document Type: Conference Paper

References (25)

1. Banzhaf W., Nordin P., Keller R. E., and Francone F. D., 1998. Genetic Programming An Introduction: On the Automatic Evolution of Computer Programs and Its Applications, San Francisco, CA: Morgan Kaufmann.
2. Bhattacharyya, S., 1998. Direct Marketing Response Models using Genetic Algorithms. In R. Agrawal, P. Stolorz, and G. Piatetsky-Shapiro Eds. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. 144-148. CA. AAAI Press.
3. Breiman L., Friedman, J. H., Olshen, R. A., Stone, C. J., 1984 Classification and regression trees. Monterey CA: Wadsworth & Brooks/Cole.
4. Chellapilla, K., 1997. Evolutionary Programming without Subtree Crossover, IEEE Trans. On Evolutionary Computation, vol. 1, no. 3, pp. 209-216.
5. Domingos, P., 1998. Occam's Two Razors: The Sharp and the Blunt. In R. Agrawal, P. Stolorz, and G. Piatetsky-Shapiro Eds. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. 144-148. CA. AAAI Press.
6. Efron B. and Gong, G., 1983. A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation. American Statistician 37:36-48
7. Fayyad, U., Piatetsky-Shapiro, G., and Smyth. P., 1996. The KDD Process for Extracting useful Knowledge from Volumes of Data. Communications of the Association for Computing Machinery, 27-34 Vol. 39, No. 11.
8. Fogel, D. B., 1995. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, Piscataway, NJ. IEEE Press.
9. Iba H., Kurita T., de Garis H., and Sato T., System identification using structured genetic algorithms, Proceedings from the Fifth International Conference on Genetic Algorithms, 279-286. Morgan Kaufmann.
10. KDD-96, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, Evangelos Simoudis, Jiawei Han, and Usama Fayyad, Eds. Menlo Park, CA, AAAI Press.
11. KDD-97, Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, David Heckerman, Heikki Mannila, Daryl Pregibon, and Ramasamy Uthurusamy, Eds. Menlo Park, CA, AAAI Press.
12. KDD-98, Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining Rakesh Agrawal, Paul Stolorz and Gregory Piatetsky-Shapiro, Eds. Menlo Park, CA, AAAI Press.
13. Koza, J. R., 1992. Genetic Programming: On the programming of computer by means of natural selection. Cambridge, MA: MIT Press
14. Koza, J. R., 1994. Genetic Programming II: Automatic Discovery of Reusable Subprograms. Cambridge, MA: MIT Press.
15. Michie, D. Spiegelhalter, D. Taylor C. C. 1994. Machine Learning, Neural and Statistical Classification, Ellis Horwood.
16. Mueller, W.; Wysotzki, F. 1997. The decision tree algorithm CAL5 based on a statistical approach to its splitting algorithm. In Nakhaeizadeh, G.; Taylor, C. C. Eds. Machine learning and statistics. 45-65 New York, NY. Wiley.
17. Murphy, S., and Pazzani, M. 1994. Exploring the decision forest: an empirical investigation of Occam's razor in decision tree induction. Journal of Artificial Intelligence Research 1:257-275.
18. Ng, A. Y., 1997. Preventing "Overfitting" of Cross-Validation data. Proceedings of the Fourteenth International Conference on Machine Learning. 245-253. Nashville, TN: Morgan Kaufmann.
19. Quinlan, J. R., C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA, 1993.
20. Schaffer, C., 1994. Overfitting avoidance as bias. Machine Learning 10:153-178.
21. Webb, G. I., 1997 Decision Tree grafting. Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, 846-851. Nagoya, Japan: Morgan Kaufmann.
22. Vapnik, N. N., 1995. The Nature of Statistical Learning Theory. New York, NY: Springer-Verlag
23. Wolpert, D., 1996. The lack of a priori distinctions between learning algorithms. Neural Computation 8:1341-1390.
24. Zhang, B.-T., 1997. A Taxonomy of Control Schemes for Genetic Code Growth. Workshop on Evolutionary Computation with Variable Size Representation at the International Conference on Genetic Algorithms.
25. Zhang, B.-T. and Muhlenbein, H., Balancing Accuracy and Parsimony in Genetic Programming, Evolutionary Computation, 17-28 Vol. 3, No. 1