NDLI: Pruning Decision Tree Using Genetic Algorithms

Content Provider	IEEE Xplore Digital Library
Author	Jie Chen Xizhao Wang Junhai Zhai
Copyright Year	2009
Abstract	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.
Starting Page	244
Ending Page	248
File Size	266068
Page Count	5
File Format	PDF
ISBN	9781424438358
DOI	10.1109/AICI.2009.351
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning algorithms Mathematics Genetic algorithms Computer science Genetic algorithm Machine learning Inference algorithms Decision trees Artificial intelligence Computational intelligence Pruning decision tree Testing Overfitting
Content Type	Text
Resource Type	Article

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