摘要
决策树是数据挖掘中的一种高效方法,但是当训练数据的属性很多时,构建的决策树的规模会随属性个数增加而指数级增长,进而会产生海量的规则。针对该问题,提出了一种基于遗传算法的优化方法。首先根据信息增益利用轮盘赌方法选取若干组属性,构建多棵决策树,然后利用遗传算法对多棵决策树进行组合,并最终形成规则集。最后给出了实验结果,证明了该方法的可行性和有效性。
Decision tree is a highly effective method in data mining.However,the number of decision tree node will be exponential growth with the increament of attributes.Therefore,there are too many rules when the number of attributes is large.To avoiding this problem,the paper presents an optimization method based on genetic algorithm.Firstly,choose some groups of attribute with roulette wheel method based on information gain,construct decision trees with these attributes,and then,genetic algorithm is used to recombine them.So the rule set could be created with the result of the combination.Finally,the results of the experiments indicate that the method is feasible and effective.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第3期148-151,共4页
Computer Engineering and Applications
关键词
数据挖掘
决策树
信息增益
遗传算法
data mining
decision tree
information gain
genetic algorithm
