【摘要】 为了改善树增强朴素贝叶斯(TAN)的分类精度,对TAN结构进行了扩展,提出了一种利用可分解的评分函数构建树形贝叶斯网络分类模型的学习方法。在构建TAN网络时允许属性没有父结点。采用低阶CI测试初步剔涂无效属性,再结合改进的BIC评分函数利用贪婪搜索获得每个属性结点的父结点,从而建立分类模型。对比朴素贝叶斯(NB)和TAN,提出的分类算法在分类准确率和AUC面积两个指标上表现更好,说明本文模型拥有比TAN更好的分类效果。更多还原

【Abstract】 In order to improve the classification accuracy of Tree Augmented Na?ve Bayes(TAN),extended the TAN structure,and proposed a learning method of building the tree-structure Bayesian network classifier with using a decomposable scoring function.When constructing a classification network,allows each attribute to have no parent node or only one attribute parent node.Applies the low-order CI test to eliminate die useless attribute firstly,and then based on improved BIC function,acquires the parent node of each attribute node with the greedy algotithom,to establish the classification model.The modified classifier behaves better than NB and TAN on both the classification accuracy and the AUC area,which proves that the proposed model performs better than TAN.更多还原