An Improved Selective Ensemble Method for Spam Filtering

被引：0

作者：

Cai, Jinye ^{[1
,2
]}

Xu, Pingping ^{[1
,2
]}

Tang, Huiyu ^{[3
]}

Sun, Lin ^{[1
,2
]}

机构：

[1] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 210096, Jiangsu, Peoples R China

[2] Southeast Univ, Jiangsu Prov Key Lab Sensor Network Technol, Wuxi 214135, Peoples R China

[3] Waseda Univ, Grad Sch IPS, Kitakyushu, Fukuoka 8080135, Japan

来源：

2013 15TH IEEE INTERNATIONAL CONFERENCE ON COMMUNICATION TECHNOLOGY (ICCT) | 2013年

关键词：

Text mining; Classification; Spam filtering; SVM; Clustering; Selective ensemble;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents an improved method of selective ensemble to filter the spam messages. The design adopts clustering based on the diversity between sub-classifiers to solve the problem of selection. To improve accuracy and stability, a conception of confidence weight is proposed to evaluate the reliability of selected sub-classifiers. The training model is created with small datasets as in the real situation. For practical usage, this method only uses 150 samples of user's file and executes bootstrapping between 50 and 70 times on them. Experiments validate the effectiveness of this method in handling the spam filtering problem.

引用

页码：743 / 747

页数：5

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