TY - GEN
T1 - Pruning decision tree using genetic algorithms
AU - CHEN, Jie
AU - WANG, Xizhao
AU - ZHAI, Junhai
N1 - This research is supported by the Natural Science Foundation of Hebei Province (F2008000635), by the key project foundation of applied fundamental research of Hebei Province (08963522D), by the plan of 100 excellent innovative scientists of the first group in Education Department of Hebei Province, and by the Scientific Research Foundation of Hebei Province (06213548).
PY - 2009
Y1 - 2009
N2 - Genetic algorithm is one of the commonly used approaches on machine learning. In this paper, we put forward a genetic algorithm approach for pruning decision tree. Binary coding is adopted in which an individual in a population consists of a fixed number of weight that stand for a solution candidate. The evaluation function considers error rate of decision tree over the test set. Three common operators for genetic algorithm such as random mutation and single-point crossover is applied for the population. Finally the algorithm returns an individual with the highest fitness as a local optimal weight. Based on four databases from UCI, we compared our approach with several other traditional decision tree pruning techniques including cost-complexity pruning, Pessimistic Error Pruning and Reduced error pruning. The results show that our approach has an better or equal effect with other pruning method.
AB - Genetic algorithm is one of the commonly used approaches on machine learning. In this paper, we put forward a genetic algorithm approach for pruning decision tree. Binary coding is adopted in which an individual in a population consists of a fixed number of weight that stand for a solution candidate. The evaluation function considers error rate of decision tree over the test set. Three common operators for genetic algorithm such as random mutation and single-point crossover is applied for the population. Finally the algorithm returns an individual with the highest fitness as a local optimal weight. Based on four databases from UCI, we compared our approach with several other traditional decision tree pruning techniques including cost-complexity pruning, Pessimistic Error Pruning and Reduced error pruning. The results show that our approach has an better or equal effect with other pruning method.
KW - Genetic algorithm
KW - Overfitting
KW - Pruning decision tree
UR - http://www.scopus.com/inward/record.url?scp=77949283173&partnerID=8YFLogxK
U2 - 10.1109/AICI.2009.351
DO - 10.1109/AICI.2009.351
M3 - Conference paper (refereed)
AN - SCOPUS:77949283173
SN - 9780769538358
T3 - International Conference on Artificial Intelligence and Computational Intelligence (AICI)
SP - 244
EP - 248
BT - Proceedings : 2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009
PB - IEEE
T2 - 2009 International Conference on Artificial Intelligence and Computational Intelligence, AICI 2009
Y2 - 7 November 2009 through 8 November 2009
ER -