基于BIC测度和遗传算法的TANC结构学习

Structure Learning of TANC Based on BIC and Genetic Algorithms

树扩展朴素贝叶斯分类器(TANC)是实用性较强的一种分类器,其性能优于朴素贝叶斯分类器。现有的TANC结构学习算法有基于互信息测度的相关性分析方法和贝叶斯信息测度(BIC)的搜索打分方法。将遗传算法引入TANC结构学习,用BIC作为评价函数,提出了基于BIC测度和遗传算法的TANC结构学习算法GA-TANC,并以此构建分类器,用分类准确率衡量算法的性能。实验结果表明,GA-TANC算法有更高的分类准确率,从而说明GA-TANC结构学习算法是准确有效的。

Tree Augmented Naive Bayesian Classifier （TANC） is a type of quite applied classifier, its performance is superior to Naive Bayesian Classifier. Existing TANC structure learning algorithm are based on relativity analysis using mutual information criterion or based on search ＆ scoring using Bayesian information criterion. Using BIC as evaluate function, this paper introduces genetic algorithm into TANC structure learning, and proposes a new TANC structure learning algorithm based on BIC and genetic algorithm. Using classification accuracy to scale classification performance. Experiment results show that GA - TANC is accurate and effective.