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基于TAN的文本自动分类框架 被引量：1

Automatic Text Categorization Framework Based on TAN
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摘要 介绍一种树状朴素贝叶斯(TAN)文本分类模型,对该模型存在的阈值选取问题进行实验分析,提出不需要进行阈值选取的TAN文本自动分类框架(ATAN)。在中英文非均匀类分布测试集上对基于ATAN的2种算法与手动选取阈值达到最优性能的BL-TAN进行对比,结果表明基于ATAN的算法具有更高性能。 This paper introduces a Tree-Augmented Na？ve Bayes（TAN） text categorization model,analyzes its problem of threshold selection,and proposes an Automatic TAN（ATAN） text categorization framework.Two algorithms based on ATAN are compared to the BL-TAN with the best classification performance at a specified threshold both on Chinese and English imbalanced datasets.Results show that algorithms based on ATAN have higher performance than BL-TAN.
作者 刘佳 贾彩燕
机构地区 北京交通大学计算机与信息技术学院
出处 《计算机工程》 CAS CSCD 北大核心 2010年第16期36-38,41,共4页 Computer Engineering
基金 高等学校博士学科点专项科研基金资助项目(2007004038)
关键词 文本分类 树状朴素贝叶斯模型 贝叶斯网络 text categorization Tree-Augmented Naive Bayes(TAN)model Bayesian network
分类号 TP391 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献7

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二级参考文献15

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计算机工程
2010年 第16期

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