MulO-AntMiner: a new ant colony algorithm for the multi-objective classification problem
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part II
Decision tree selection in an industrial machine fault diagnostics
MEDI'12 Proceedings of the 2nd international conference on Model and Data Engineering
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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.