Applications of Genetic Programming in Data Mining

This paper details the application of a genetic programming framework for induction of useful classification rules from a database of income statements, balance sheets, and cash flow statements for North American public companies. Potentially interesting classification rules are discovered. Anomalies in the discovery process merit further investigation of the application of genetic programming to the dataset for the problem domain.

Authors:

• Saleh Mesbah Elkaffas
• Ahmed A. Toony

Keywords:

• Genetic programming
• data mining classification rule.

References:

[1] Little, J. B. and Rhodes, L., "Understanding Wall Street", Liberty
Publishing Company, Cockeysville, MD, 1983.
[2] Fayyad, U.M., Piattetsky-Shapiro, G., and Smyth, P., "From data mining
to knowledge discovery: an overview. Advances in Knowledge
Discovery and Data Mining", 1-34. AAAI/MIT Press, 1996.
[3] COMPUSTAT, http://www.compustat.com/www/db/na_descr.html,
Standard & Poors Institutional Market Services, Englewood, CO, USA,
2001.
[4] Fidelis, M.V., Lopes, H.S., and Freitas, A.A., "Discovering
comprehensible classification rules with a genetic algorithm", Proc.
Congress on Evolutionary Computation - 2000 (CEC-2000), 805-810.
La Jolla, CA, USA, 2000.
[5] Freitas, A.A., "A genetic programming framework for two data mining
tasks: classification and generalized rule induction", Genetic
Programming 1997: Proc. 2nd Annual Conf. (Stanford University, July
1997), 96-101. Morgan Kaufmann, 1997.
[6] Goldberg, David E., "Genetic Algorithms in Search, Optimization, and
Machine Learning", Addison Wesley Longman, Inc, 1989.
[7] Koza, John R., "Genetic Programming: On the Programming of
Computers by Means of Natural Selection", Cambridge, MA: The MIT
Press, 1992.