| Incrementally Exploiting Sentential Association for Email Classification |
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| 引用本文： | 李曲 何玉 冯剑琳 冯玉才. Incrementally Exploiting Sentential Association for Email Classification[J]. 西南交通大学学报(英文版), 2006, 14(2): 129-134 |
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| 作者姓名： | 李曲 何玉 冯剑琳 冯玉才 |
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| 作者单位： | Department of Computer Science, Huazhong University of Science and Technology, Wuhan 430074, China |
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| 基金项目： | Foundation item The National Natural Science Foundation of China （No. 60303030） and the Natural Science Foundation of Chongqing （ No. 8721 ） |
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| 摘 要： | Introduction Email classification can be cast into text classifi-cation (TC). Many methods have been proposed forTC, which can be extended to Email domain. Popularmethods include NaiveBayes, decision trees, supportvector machine (SVM) and association rule…
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| 关 键 词： | 电子邮件 识别系统 分组方法 系统设计 |
| 文章编号： | 1005-2429（2006）02-0129-06 |
| 收稿时间： | 2005-05-20 |
Incrementally Exploiting Sentential Association for Email Classification |
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| Li Qu,He Yu,Feng Jianlin,Feng Yucai. Incrementally Exploiting Sentential Association for Email Classification[J]. Journal of Southwest Jiaotong University, 2006, 14(2): 129-134 |
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| Authors: | Li Qu He Yu Feng Jianlin Feng Yucai |
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| Abstract: | A novel association-based algorithm EmailinClass is proposed for incremental Email classification. In view of the fact that the basic semantic unit in an Email is actually a sentence, and the words within the same sentence are typically more semantically related than the words that just appear in the same Email, EmailInClass views a sentence rather than an Email as a transaction. Extensive experiments conducted on benchmark corpora Enron reveal that the effectiveness of EmallInClass is superior to the non-incremental alternatives such as NalveBayes and SAT-MOD. In addition, the classification rules generated by EroaillnClass are human readable and revisable, |
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| Keywords: | Document Requent itemset Category frequent itemset MODFIT heuristic Category prefix-tree Incremental classification |
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