Information theory based feature valuing for logistic regression for spam filtering

Haoliang Qi; Xiaoning He; Yong Han; Muyun Yang; Sheng Li

doi:10.1109/IALP.2010.65

Information theory based feature valuing for logistic regression for spam filtering

Haoliang Qi^*
, Xiaoning He
, Yong Han
, Muyun Yang
, Sheng Li

^*Corresponding author for this work

Heilongjiang Institute of Technology
School of Computer Science and Technology, Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Discriminative learning models such as Logistic Regression (LR) has shown good performance in spam filtering tasks. While most previous researches on LR have used binary features, this discards much useful information. To overcome this problem, information theory based feature valuing method for LR instead of traditional binary features is presented. The effectiveness of our approach has been evaluated on TREC, CEAS, and SEWM test sets. Results show that the proposed method outperforms the traditional binary features in the most test sets.

Original language	English
Title of host publication	Proceedings - 2010 International Conference on Asian Language Processing, IALP 2010
Pages	166-169
Number of pages	4
DOIs	https://doi.org/10.1109/IALP.2010.65
State	Published - 2010
Externally published	Yes
Event	2010 International Conference on Asian Language Processing, IALP 2010 - Harbin, China Duration: 28 Dec 2010 → 30 Dec 2010

Publication series

Name	Proceedings - 2010 International Conference on Asian Language Processing, IALP 2010

Conference

Conference	2010 International Conference on Asian Language Processing, IALP 2010
Country/Territory	China
City	Harbin
Period	28/12/10 → 30/12/10

Keywords

Feature valuing
Informatin theory
Logistic regression
Spam fitering

Access to Document

10.1109/IALP.2010.65

Cite this

Qi, H., He, X., Han, Y., Yang, M., & Li, S. (2010). Information theory based feature valuing for logistic regression for spam filtering. In Proceedings - 2010 International Conference on Asian Language Processing, IALP 2010 (pp. 166-169). Article 5681605 (Proceedings - 2010 International Conference on Asian Language Processing, IALP 2010). https://doi.org/10.1109/IALP.2010.65

@inproceedings{03c57c90742c4e4d87c251a3514c5c6b,

title = "Information theory based feature valuing for logistic regression for spam filtering",

abstract = "Discriminative learning models such as Logistic Regression (LR) has shown good performance in spam filtering tasks. While most previous researches on LR have used binary features, this discards much useful information. To overcome this problem, information theory based feature valuing method for LR instead of traditional binary features is presented. The effectiveness of our approach has been evaluated on TREC, CEAS, and SEWM test sets. Results show that the proposed method outperforms the traditional binary features in the most test sets.",

keywords = "Feature valuing, Informatin theory, Logistic regression, Spam fitering",

author = "Haoliang Qi and Xiaoning He and Yong Han and Muyun Yang and Sheng Li",

year = "2010",

doi = "10.1109/IALP.2010.65",

language = "英语",

isbn = "9780769542881",

series = "Proceedings - 2010 International Conference on Asian Language Processing, IALP 2010",

pages = "166--169",

booktitle = "Proceedings - 2010 International Conference on Asian Language Processing, IALP 2010",

note = "2010 International Conference on Asian Language Processing, IALP 2010 ; Conference date: 28-12-2010 Through 30-12-2010",

}

Qi, H, He, X, Han, Y, Yang, M & Li, S 2010, Information theory based feature valuing for logistic regression for spam filtering. in Proceedings - 2010 International Conference on Asian Language Processing, IALP 2010., 5681605, Proceedings - 2010 International Conference on Asian Language Processing, IALP 2010, pp. 166-169, 2010 International Conference on Asian Language Processing, IALP 2010, Harbin, China, 28/12/10. https://doi.org/10.1109/IALP.2010.65

Information theory based feature valuing for logistic regression for spam filtering. / Qi, Haoliang; He, Xiaoning; Han, Yong et al.
Proceedings - 2010 International Conference on Asian Language Processing, IALP 2010. 2010. p. 166-169 5681605 (Proceedings - 2010 International Conference on Asian Language Processing, IALP 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Information theory based feature valuing for logistic regression for spam filtering

AU - Qi, Haoliang

AU - He, Xiaoning

AU - Han, Yong

AU - Yang, Muyun

AU - Li, Sheng

PY - 2010

Y1 - 2010

N2 - Discriminative learning models such as Logistic Regression (LR) has shown good performance in spam filtering tasks. While most previous researches on LR have used binary features, this discards much useful information. To overcome this problem, information theory based feature valuing method for LR instead of traditional binary features is presented. The effectiveness of our approach has been evaluated on TREC, CEAS, and SEWM test sets. Results show that the proposed method outperforms the traditional binary features in the most test sets.

AB - Discriminative learning models such as Logistic Regression (LR) has shown good performance in spam filtering tasks. While most previous researches on LR have used binary features, this discards much useful information. To overcome this problem, information theory based feature valuing method for LR instead of traditional binary features is presented. The effectiveness of our approach has been evaluated on TREC, CEAS, and SEWM test sets. Results show that the proposed method outperforms the traditional binary features in the most test sets.

KW - Feature valuing

KW - Informatin theory

KW - Logistic regression

KW - Spam fitering

UR - https://www.scopus.com/pages/publications/79551559577

U2 - 10.1109/IALP.2010.65

DO - 10.1109/IALP.2010.65

M3 - 会议稿件

AN - SCOPUS:79551559577

SN - 9780769542881

T3 - Proceedings - 2010 International Conference on Asian Language Processing, IALP 2010

SP - 166

EP - 169

BT - Proceedings - 2010 International Conference on Asian Language Processing, IALP 2010

T2 - 2010 International Conference on Asian Language Processing, IALP 2010

Y2 - 28 December 2010 through 30 December 2010

ER -

Information theory based feature valuing for logistic regression for spam filtering

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this