Maximum Entropy Framework Used in Text Classification

摘要：

In this paper, Maximum Entropy (ME) framework is used to classify text documents. The ME framework has a lot of advantages when compared with other supervised learning algorithms, such as naive Bayes classifier. For example, it makes no inherent condi tional independence assumptions between terms. With four labeled data sets, extensive experiments are made to compare the accuracy of ME algorithm with those of naive Bayes and Support Vector Machine (SVM), which are two popular and accurate algorithms in the domain of text classification. The final result is that ME method consistently outperforms naive Bayes and SVM algorithms in accuracy. On the WebKB and In dustry Vector data sets, the accuracy of ME algorithm increases from 81.38％ to 85.52％ and from 85.73％ to 89.78％ respectively. On the third 20 Newsgroups data set, our experimental result is opposite to that of Nigam et al. For the last Reuters-21578 data set, the accuracy of ME algorithm increases from 94.76％ to 96.16％.

作者: Hui Wang Lin Wang Lixia Yi

作者单位: College of Computer Science and Information Engineering Tianjin University of Science and Technology College of Marine Science and Engineering Tianjin University of Science and Technology Tianjin 30045

会议类型: 国际会议

会议名称: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems(2010 IEEE 智能计算与智能系统国际会议 ICIS 2010)

会议地点: 厦门

会议语种:英文

页码: 828-833

在线出版日期: 2010-10-29（万方平台首次上网日期，不代表论文的发表时间）