A Safe Approach to Shrink Email Sample Set while Keeping Balance between Spam and Normal

To deal with any possible cases for training anti-spam machine learning models, it is crucial to design a safe way to shrink the size of training sample set via reducing redundancies with minimal information loss for classification as well as make distribution of samples balanced. Presently, there i...

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Bibliographic Details
Published in2009 Third IEEE International Conference on Secure Software Integration and Reliability Improvement pp. 329 - 334
Main Authors Lili Diao, Hao Wang
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
anti-spam
Conferences
Engines
Industry applications
Machine learning
Software safety
Software testing
Software tools
Support vector machine classification
Support vector machines
SVM
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Summary:To deal with any possible cases for training anti-spam machine learning models, it is crucial to design a safe way to shrink the size of training sample set via reducing redundancies with minimal information loss for classification as well as make distribution of samples balanced. Presently, there is no such solution to do so. In this paper, we propose a safe approach to address these problems and improve the quality of training email sample pool (set) for getting high quality machine learning models for better anti-spam engine with non-biased high spam detection rates as well as low false positive rates.
ISBN:0769537588
9780769537580
DOI:10.1109/SSIRI.2009.66