属性值加权的一依赖估测器模型分类算法研究

Research on Classification Algorithm Combining Weighted Attribute Valueswith One-Dependence Estimator Model

分类问题是数据挖掘和机器学习领域研究的重点问题,贝叶斯网络模型因其简单高效的特点而广泛应用于分类问题。一依赖估测器(ODE)模型作为半监督学习贝叶斯网络模型中的经典模型,受到研究人员的广泛关注。现有的ODE模型分类器在进行分类判别时,未考虑不同的属性节点作为根节点时对分类过程的贡献不同,为此,将ODE模型分类器与属性值加权方法相结合并提出MI-ODE算法。采用相互信息(MI)度量属性根节点的属性值与类变量之间的依赖关系并作为ODE模型的权值,对ODE分类器模型进行属性值加权平均。将MI-ODE算法应用于现实分类问题的36个标准数据集,结果表明,相比于NB算法、AODE算法与TAN算法,该算法的分类性能更优。

In the field of data mining and machine learning,classification is a key problem to which the Bayesian network model is frequently applied due to its simplicity and high efficiency.As a classical Bayesian network model for semi-supervised learning,One-Dependence Estimator(ODE)has been widely concerned by researchers.However,the existing ODE model classifiers do not consider the varying contribution of different attribute nodes acting as root nodes to the classification process.Therefore,this paper combines ODE model classifier with the attribute value weighting method,and on this basis proposes the MI-ODE algorithm.The algorithm adopts Mutual Information(MI)to measure the dependence between attribute values and class variables of the attribute root node,which is used as the weight of the ODE model.Then weighted average is implemented for the attribute values of the ODE classifier model.The MI-ODE algorithm is tested on 36 standard data sets for real-world classification problems,and results show that compared with NB algorithm,AODE algorithm and TAN algorithm,the proposed algorithm has better classification performance.