IEICE TRANSACTIONS on Information
Intelligent Email Categorization Based on Textual Information and Metadata
Summary :
A set of systematic experiments on intelligent email categorization has been conducted with different machine learning algorithms applied to different parts of data in order to achieve the most correct classification. The categorization is based on not only the body but also the header of an email message. The metadata (e.g. sender name, sender organization, etc.) provide additional information that can be exploited to improve the categorization capability. Results of experiments on real email data demonstrate the feasibility of our approach to find the best learning algorithm and the metadata to be used, which is a very significant contribution in email classification. It is also shown that categorization based only on the header information is comparable or superior to that based on all the information in a message for all the learning algorithms considered.
- Publication
- IEICE TRANSACTIONS on Information Vol.E86-D No.7 pp.1280-1288
- Publication Date
- 2003/07/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Cognitive Science
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Jihoon YANG, Venkat CHALASANI, Sung-Yong PARK, "Intelligent Email Categorization Based on Textual Information and Metadata" in IEICE TRANSACTIONS on Information,
vol. E86-D, no. 7, pp. 1280-1288, July 2003, doi: .
Abstract: A set of systematic experiments on intelligent email categorization has been conducted with different machine learning algorithms applied to different parts of data in order to achieve the most correct classification. The categorization is based on not only the body but also the header of an email message. The metadata (e.g. sender name, sender organization, etc.) provide additional information that can be exploited to improve the categorization capability. Results of experiments on real email data demonstrate the feasibility of our approach to find the best learning algorithm and the metadata to be used, which is a very significant contribution in email classification. It is also shown that categorization based only on the header information is comparable or superior to that based on all the information in a message for all the learning algorithms considered.
URL: https://global.ieice.org/en_transactions/information/10.1587/e86-d_7_1280/_p
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@ARTICLE{e86-d_7_1280,
author={Jihoon YANG, Venkat CHALASANI, Sung-Yong PARK, },
journal={IEICE TRANSACTIONS on Information},
title={Intelligent Email Categorization Based on Textual Information and Metadata},
year={2003},
volume={E86-D},
number={7},
pages={1280-1288},
abstract={A set of systematic experiments on intelligent email categorization has been conducted with different machine learning algorithms applied to different parts of data in order to achieve the most correct classification. The categorization is based on not only the body but also the header of an email message. The metadata (e.g. sender name, sender organization, etc.) provide additional information that can be exploited to improve the categorization capability. Results of experiments on real email data demonstrate the feasibility of our approach to find the best learning algorithm and the metadata to be used, which is a very significant contribution in email classification. It is also shown that categorization based only on the header information is comparable or superior to that based on all the information in a message for all the learning algorithms considered.},
keywords={},
doi={},
ISSN={},
month={July},}
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TY - JOUR
TI - Intelligent Email Categorization Based on Textual Information and Metadata
T2 - IEICE TRANSACTIONS on Information
SP - 1280
EP - 1288
AU - Jihoon YANG
AU - Venkat CHALASANI
AU - Sung-Yong PARK
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E86-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2003
AB - A set of systematic experiments on intelligent email categorization has been conducted with different machine learning algorithms applied to different parts of data in order to achieve the most correct classification. The categorization is based on not only the body but also the header of an email message. The metadata (e.g. sender name, sender organization, etc.) provide additional information that can be exploited to improve the categorization capability. Results of experiments on real email data demonstrate the feasibility of our approach to find the best learning algorithm and the metadata to be used, which is a very significant contribution in email classification. It is also shown that categorization based only on the header information is comparable or superior to that based on all the information in a message for all the learning algorithms considered.
ER -
English
