强化属性依赖关系的K阶贝叶斯分类模型

经典K阶贝叶斯分类模型(KDB)进行属性排序时,仅考虑类变量与决策属性间的直接相关,而忽略以决策属性为条件二者之间的条件相关.针对以上问题,在KDB结构的基础上,以充分表达属性间的依赖信息为原则,强化属性间的依赖关系,提升决策属性对分类的决策表达,利用类变量与决策属性间的条件互信息优化属性次序,融合属性约简策略剔除冗余属性,降低模型结构复杂带来的过拟合风险,根据贪婪搜索策略选择最优属性并构建模型结构.在UCI机器学习数据库中数据集的实验结果表明,该模型相比于KDB而言,具有更好的分类精度和突出的鲁棒性.

K-dependence Bayesian classifiers for strengthening attribute dependencies

When the attributes are ordered, the K-dependence Bayesian(KDB) classifiers only consider direct dependence between the classes and the decision attributes, but neglect the conditional correlation between them with the decision attribute as a condition. To fully express the dependency relationship between the classes and attributes and improve the expression of decision attributes for classification, we use conditional relationship between the classes as and the decision attributes to find an optimal attribute order based on the KDB structure. Besides, attribute selection is used to remove redundant attributes and prevent over-fitting. The structure learning and attribute selection are carried out based on the greedy search strategy. The proposed algorithm is tested on several UCI datasets and compared with other classical methods. The results show that the proposed approach obtains higher classification accuracy and outstanding robustness.