Improved text classification algorithm for spam filtering based on CABSOFV

2013 ◽  
Author(s):  
G. Y. Wei ◽  
L. Zou ◽  
J. Pan
Keyword(s):  
Text Classification ◽  
Classification Algorithm ◽  
Spam Filtering

scholarly journals Short Text Classification Algorithm Based on Semi-Supervised Learning and SVM

2015 ◽  
Vol 10 (12) ◽  
pp. 195-206 ◽  
Author(s):  
Chunyong Yin ◽  
Jun Xiang ◽  
Hui Zhang ◽  
Zhichao Yin ◽  
Jin Wang
Keyword(s):  
Supervised Learning ◽  
Text Classification ◽  
Classification Algorithm ◽  
Short Text

A Linear Text Classification Algorithm Based on Category Relevance Factors

2002 ◽  
pp. 88-98 ◽  
Author(s):  
Zhi-Hong Deng ◽  
Shi-Wei Tang ◽  
Dong-Qing Yang ◽  
Ming Zhang ◽  
Xiao-Bin Wu ◽  
...  
Keyword(s):  
Text Classification ◽  
Classification Algorithm ◽  
Linear Text

A Message Topic Model for Multi-Grain SMS Spam Filtering

2016 ◽  
Vol 12 (2) ◽  
pp. 83-95 ◽  
Author(s):  
Jialin Ma ◽  
Yongjun Zhang ◽  
Zhijian Wang ◽  
Kun Yu
Keyword(s):  
Text Classification ◽  
Short Message Service ◽  
Topic Model ◽  
Short Message ◽  
Spam Filtering ◽  
The Public ◽  
Data Problem ◽  
Message Service ◽  
Noise Data ◽  
Sparse Data Problem

At present, content-based methods are regard as the more effective in the task of Short Message Service (SMS) spam filtering. However, they usually use traditional text classification technologies, which are more suitable to deal with normal long texts; therefore, it often faces some serious challenges, such as the sparse data problem and noise data in the SMS message. In addition, the existing SMS spam filtering methods usually consider the SMS spam task as a binary-class problem, which could not provide for different categories for multi-grain SMS spam filtering. In this paper, the authors propose a message topic model (MTM) for multi-grain SMS spam filtering. The MTM derives from the famous probability topic model, and is improved in this paper to make it more suitable for SMS spam filtering. Finally, the authors compare the MTM with the SVM and the standard LDA on the public SMS spam corpus. The experimental results show that the MTM is more effective for the task of SMS spam filtering.

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