GA Tree: genetically evolved decision trees

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	Similar Articles Articles by A. Papagelis Articles by D. Kalles

12th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'00)

Vancouver, British Columbia, Canada

November 13-November 15

ISBN: 0-7695-0909-6

	ASCII Text	x
	A. Papagelis, D. Kalles, "GA Tree: genetically evolved decision trees," Tools with Artificial Intelligence, IEEE International Conference on, pp. 0203, 12th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'00), 2000.

	BibTex	x
	@article{ 10.1109/TAI.2000.889871, author = {A. Papagelis and D. Kalles}, title = {GA Tree: genetically evolved decision trees}, journal ={Tools with Artificial Intelligence, IEEE International Conference on}, volume = {0}, year = {2000}, isbn = {0-7695-0909-6}, pages = {0203}, doi = {http://doi.ieeecomputersociety.org/10.1109/TAI.2000.889871}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Tools with Artificial Intelligence, IEEE International Conference on TI - GA Tree: genetically evolved decision trees SN - 0-7695-0909-6 SP EP A1 - A. Papagelis, A1 - D. Kalles, PY - 2000 KW - genetic algorithms; decision trees; search problems; learning (artificial intelligence); GA Tree; genetically evolved decision trees; genetic algorithms; classification decision tree evolution; system performance; standard discretized concept learning problems; C4 5; OneR VL - 0 JA - Tools with Artificial Intelligence, IEEE International Conference on ER -

A. Papagelis, Comput. Technol. Inst., Patras, Greece

D. Kalles, Comput. Technol. Inst., Patras, Greece

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TAI.2000.889871

Abstract: We use genetic algorithms to evolve classification decision trees. The performance of the system is measured on a set of standard discretized concept learning problems and compared (very favorably) with the performance of two known algorithms (C4.5, OneR).

Index Terms:

genetic algorithms; decision trees; search problems; learning (artificial intelligence); GA Tree; genetically evolved decision trees; genetic algorithms; classification decision tree evolution; system performance; standard discretized concept learning problems; C4 5; OneR

Citation:

A. Papagelis, D. Kalles, "GA Tree: genetically evolved decision trees," ictai, pp.0203, 12th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'00), 2000

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