| 三种中文文本自动分类算法的比较和研究 |
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| 引用本文： | 陈琳,王箭. 三种中文文本自动分类算法的比较和研究[J]. 计算机与现代化, 2012, 0(2): 1-4,7 |
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| 作者姓名： | 陈琳 王箭 |
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| 作者单位： | 南京航空航天大学信息科学与技术学院,江苏南京210016 |
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| 基金项目： | 国家863计划资助项目（2009AA044601） |
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| 摘 要： | 网络信息规模随着互联网与信息技术的发展而不断增大,在这些信息中,各种类型的文本信息占据了相当大的比重。因此,高效、快速地对文本信息进行分类是网络信息处理中一个关键问题。本文分析比较了SVM算法、朴素Bayes算法和KNN算法3种算法,并通过实验证明了这3种算法在中文文本分类中的效果。实验结果表明:SVM算法比KNN算法和朴素Bayes算法更优,SVM算法是一种较好的中文文本分类算法。
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| 关 键 词： | 中文文本分类 SVM Bayes KNN |
Comparison and Research on Algorithms of Three Chinese Text Classification |
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| CHEN Lin,WANG Jian. Comparison and Research on Algorithms of Three Chinese Text Classification[J]. Computer and Modernization, 2012, 0(2): 1-4,7 |
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| Authors: | CHEN Lin WANG Jian |
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| Affiliation: | (College of Information Science and Technology,Nanjing University Aeronautics ＆ Astronautics,Nanjing 210016,China) |
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| Abstract: | With the development of Internet and information technology,network information scale is explosively increasing.Among various type of information,the type of texts occupy a considerable proportion.Therefore,efficient and rapid classification and processing of text information in the network become a key issue.The paper analyzes and compares SVM algorithm,Bayes algorithm and KNN algorithm.By the experiments of the three algorithms in Chinese text classification,the results indicate SVM algorithm is superior than KNN algorithm and Bayes algorithm,SVM algorithm is an excellent Chinese text classification algorithm. |
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| Keywords: | Chinese text classification SVM Bayes KNN |
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