属性值加权的一依赖估测器模型分类算法研究

doi:10.19678/j.issn.1000-3428.0056498

计算机工程 ›› 2020, Vol. 46 ›› Issue (11): 315-320. doi: 10.19678/j.issn.1000-3428.0056498

• 开发研究与工程应用 • 上一篇

属性值加权的一依赖估测器模型分类算法研究

余良俊¹, 甘胜丰¹, 范正薇²

1. 湖北第二师范学院计算机学院, 武汉 430205;
2. 湖北广播电视大学继续教育学院, 武汉 430074

收稿日期:2019-11-05 修回日期:2019-12-08 发布日期:2020-01-03
作者简介:余良俊(1984-),女,副教授、博士,主研方向为数据挖掘、机器学习;甘胜丰,副教授、博士;范正薇,高级工程师。
基金资助:
湖北省自然科学基金面上项目"属性值加权的贝叶斯网络分类算法研究"（2018CFC893）；湖北省中央引导地方科技发展专项立项项目"区域基础教育资源配置与优化关键技术的研究与应用"（2019ZYYD012）；湖北省重点实验室开放基金项目"基于贝叶斯网络分类算法的岩爆预测研究"（KLIGIP-2018A05）；湖北省技术创新专项"土木工程智慧建造仿真交互软硬件系统"（2019AEE020）。

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

YU Liangjun¹, GAN Shengfeng¹, FAN Zhengwei²

1. College of Computer, Hubei University of Education, Wuhan 430205, China;
2. School of Continuing Education, Hubei Radio&TV University, Wuhan 430074, China

Received:2019-11-05 Revised:2019-12-08 Published:2020-01-03

摘要/Abstract

摘要： 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。

关键词: 贝叶斯网络, 一依赖估测器, 分类算法, 结构扩展, 属性值加权

Abstract: 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.

Key words: Bayesian network, One-Dependence Estimator(ODE), classification algorithm, structure extension, attribute value weighting

中图分类号:

TP301.6

余良俊, 甘胜丰, 范正薇. 属性值加权的一依赖估测器模型分类算法研究[J]. 计算机工程, 2020, 46(11): 315-320.

YU Liangjun, GAN Shengfeng, FAN Zhengwei. Research on Classification Algorithm Combining Weighted Attribute Values with One-Dependence Estimator Model[J]. Computer Engineering, 2020, 46(11): 315-320.

https://www.ecice06.com/CN/Y2020/V46/I11/315

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