Title :
GA Tree: genetically evolved decision trees
Author :
Papagelis, Athanassios ; Kalles, Dimitrios
Author_Institution :
Comput. Technol. Inst., Patras, Greece
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)
Keywords :
decision trees; genetic algorithms; learning (artificial intelligence); search problems; C4 5; GA Tree; OneR; classification decision tree evolution; genetic algorithms; genetically evolved decision trees; standard discretized concept learning problems; system performance; Current measurement; Decision trees; Gain measurement; Genetic algorithms; Impurities; Induction generators; Machine learning; Machine learning algorithms; Measurement standards; Medical diagnostic imaging;
Conference_Titel :
Tools with Artificial Intelligence, 2000. ICTAI 2000. Proceedings. 12th IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7695-0909-6
DOI :
10.1109/TAI.2000.889871
