PERC: A personal email classifier

Shih Wen Ke; Chris Bowerman; Michael Oakes

doi:10.1007/11735106_41

PERC: A personal email classifier

Shih Wen Ke, Chris Bowerman, Michael Oakes

資訊管理學系

研究成果: 書貢獻/報告類型 › 會議論文篇章 › 同行評審

5 引文斯高帕斯（Scopus）

摘要

Improving the accuracy of assigning new email messages to small folders can reduce the likelihood of users creating duplicate folders for some topics. In this paper we presented a hybrid classification model, PERC, and use the Enron Email Corpus to investigate the performance of kNN, SVM and PERC in a simulation of a real-time situation. Our results show that PERC is significantly better at assigning messages to small folders. The effects of different parameter settings for the classifiers are discussed.

原文	???core.languages.en_GB???
主出版物標題	Advances in Information Retrieval - 28th European Conference on IR Research, ECIR 2006, Proceedings
頁面	460-463
頁數	4
DOIs	https://doi.org/10.1007/11735106_41
出版狀態	已出版 - 2006
事件	28th European Conference on Information Retrieval Research, ECIR 2006 - London, United Kingdom 持續時間: 10 4月 2006 → 12 4月 2006

出版系列

名字	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
卷	3936 LNCS
ISSN（列印）	0302-9743
ISSN（電子）	1611-3349

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	28th European Conference on Information Retrieval Research, ECIR 2006
國家/地區	United Kingdom
城市	London
期間	10/04/06 → 12/04/06

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10.1007/11735106_41

引用此

Ke, S. W., Bowerman, C., & Oakes, M. (2006). PERC: A personal email classifier. 於 Advances in Information Retrieval - 28th European Conference on IR Research, ECIR 2006, Proceedings (頁 460-463). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 3936 LNCS). https://doi.org/10.1007/11735106_41

@inproceedings{d806d5c2863448409486fb85633edcd1,

title = "PERC: A personal email classifier",

abstract = "Improving the accuracy of assigning new email messages to small folders can reduce the likelihood of users creating duplicate folders for some topics. In this paper we presented a hybrid classification model, PERC, and use the Enron Email Corpus to investigate the performance of kNN, SVM and PERC in a simulation of a real-time situation. Our results show that PERC is significantly better at assigning messages to small folders. The effects of different parameter settings for the classifiers are discussed.",

author = "Ke, {Shih Wen} and Chris Bowerman and Michael Oakes",

year = "2006",

doi = "10.1007/11735106_41",

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isbn = "3540333479",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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note = "28th European Conference on Information Retrieval Research, ECIR 2006 ; Conference date: 10-04-2006 Through 12-04-2006",

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Ke, SW, Bowerman, C & Oakes, M 2006, PERC: A personal email classifier. 於 Advances in Information Retrieval - 28th European Conference on IR Research, ECIR 2006, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 卷 3936 LNCS, 頁 460-463, 28th European Conference on Information Retrieval Research, ECIR 2006, London, United Kingdom, 10/04/06. https://doi.org/10.1007/11735106_41

PERC: A personal email classifier. / Ke, Shih Wen; Bowerman, Chris; Oakes, Michael.
Advances in Information Retrieval - 28th European Conference on IR Research, ECIR 2006, Proceedings. 2006. p. 460-463 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 卷 3936 LNCS).

研究成果: 書貢獻/報告類型 › 會議論文篇章 › 同行評審

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AB - Improving the accuracy of assigning new email messages to small folders can reduce the likelihood of users creating duplicate folders for some topics. In this paper we presented a hybrid classification model, PERC, and use the Enron Email Corpus to investigate the performance of kNN, SVM and PERC in a simulation of a real-time situation. Our results show that PERC is significantly better at assigning messages to small folders. The effects of different parameter settings for the classifiers are discussed.

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PERC: A personal email classifier

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