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基于选择性集成遗传算法的BNC结构学习
引用本文:蒋望东,林士敏,鲁明羽. 基于选择性集成遗传算法的BNC结构学习[J]. 计算机辅助工程, 2006, 15(3): 46-50
作者姓名:蒋望东  林士敏  鲁明羽
作者单位:广西师范大学,计算机科学系,广西,桂林,541004;湖南财经高等专科学校,信息系,湖南,长沙,410205;广西师范大学,计算机科学系,广西,桂林,541004;大连海事大学,计算机科学与技术学院,辽宁,大连,116026
摘    要:为克服IQ算法在处理贝叶斯网络分类器(Bayesian Network Classifier,BNC)结构学习中要求先指定适合节点次序的缺点,提出GA-K2算法,将基于选择性集成的整数编码遗传算法引入到K2算法中,使之能得到最佳节点次序并且网络结构收敛到全局最优.构建贝叶斯网络分类器进行分类,实验结果表明GA-K2算法优于随意指定节点顺序的IQ算法.

关 键 词:贝叶斯网络  分类器  结构学习  K2算法  遗传算法
文章编号:1006-0871(2006)03-0046-05
收稿时间:2006-06-06
修稿时间:2006-06-06

Structure learning of BNC based on selective ensemble genetic algorithms
JIANG Wangdong,LIN Shimin,LU Mingyu. Structure learning of BNC based on selective ensemble genetic algorithms[J]. Computer Aided Engineering, 2006, 15(3): 46-50
Authors:JIANG Wangdong  LIN Shimin  LU Mingyu
Abstract:To overcome the defect that K2 algorithm requires the suitable order of nodes in advance while dealing with the structure learning of Bayesian Network Classifier (BNC), the algorithm GA-K2 is proposed which introduces the integer coding genetic algorithm based on selective ensemble concept to K2. It provides the guarantee of getting the best order of nodes and the convergence of Bayesian network structure for K2 in global optimization. The results of classification experiment by building BNC indicate that GA-K2 is better than K2 algorithm which is only with random order of nodes.
Keywords:Bayesian network   classifier   structure learning   K2 algorithm   genetic algorithm
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