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基于贝叶斯网络理论的TAN分类器无向依赖扩展 被引量：3

Extending TAN Classifiers Using Undirected Graphical Models Based on Bayesian Network Theory
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摘要 基于贝叶斯网络理论 ,对 TAN分类器进行无向网络依赖扩展 ,把属性变量之间的树结构扩展成可分解马尔科夫网络 ,使经过依赖扩展得到的分类器能够充分利用属性变量之间的依赖信息 ,提高分类能力 ,并能够通过调节阈值大小避免过度拟合 . TAN classifier is ext ended using undirected graphical models based on Bayesian network theory. Attrib ute tree is extended into decomposable markov network. As a result, Extended cl assifier can make the best of dependent information between attribute variables and classification accuracy is improved. Overfitting problem can be avoided by a djusting threshold.
作者 王双成 张邦佐 王辉 苑森淼
机构地区 上海立信会计学院信息科学系 东北师范大学计算机系 吉林大学计算机科学与技术学院
出处 《小型微型计算机系统》 CSCD 北大核心 2005年第1期42-45,共4页 Journal of Chinese Computer Systems
基金 国家自然科学基金项目 (60 2 75 0 2 6)资助
关键词 TAN分类器 可分解马尔科夫网络 贝叶斯网络 0-1损失率 最大完全子图 TAN classifier decomposable Markov network Bayesian network zero- one loss rate clique
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
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参考文献17

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  • 3Xiang Y, Wong S K M, Cercone N. A 'Microscopic' study of minimum entropy search in learning decomposable Markov networks[J]. Machine Learning. 1997, 26, 65-92.
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同被引文献13

  • 1王双成,苑森淼,王辉.基于类约束的贝叶斯网络分类器学习[J].小型微型计算机系统,2004,25(6):968-971. 被引量：30
  • 2王双成,苑森淼.具有丢失数据的贝叶斯网络结构学习研究[J].软件学报,2004,15(7):1042-1048. 被引量：62
  • 3王双成,苑森淼.具有丢失数据的可分解马尔可夫网络结构学习[J].计算机学报,2004,27(9):1221-1228. 被引量：19
  • 4Pearl J. Probabilistic reasoning in intelligent systems: networks of plausible inference [M]. San Mateo, California, Morgan Kaufmann, 1988:117 - 133.
  • 5Xiang Y, Wong S K, Cercone N. A 'Microscopic' study of minimum entropy search in learning decomposable Markov networks [J]. Machine Learning, 1997, 26(1), 65-92.
  • 6Zhen Z, Yeung R W. On characterization of entropy function via information inequalities[J]. IEEE Trans. on Information Theory, 1998, 44(4): 1440-1452.
  • 7Cheng J, Greiner R, Kelly J. Learning Bayesian networks from data: An efficient approach based on information-theory[J]. Artificial Intelligence. 2002, 137 (1 - 2) : 43 - 90.
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  • 10Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection[C] // Proceedings of the 14th International Joint Conference on Artificial Intelligence ( IJ- CAI), Montreal , Quebec , Canada, Morgan Kaufmann , 1995: 1137 - 1143.

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小型微型计算机系统
2005年 第1期

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