DocumentCode
2390903
Title
An incremental spam detection algorithm
Author
Ghanbari, Elham ; Beigy, Hamid
Author_Institution
Dept. of Comput. Eng., Sharif Univ. of Technol., Tehran, Iran
fYear
2011
fDate
15-16 June 2011
Firstpage
31
Lastpage
36
Abstract
The voluminous of the e-mails are spam. Several algorithms are represented for spam detection based on batch learning. In this paper, a new algorithm based on incremental learning is introduced. The algorithm composes new knowledge from new training data with previous knowledge by combining classifiers based on weighted majority voting. The experiment results show that the proposed algorithm outperforms other related incremental algorithms and non-incremental algorithms.
Keywords
learning (artificial intelligence); security of data; unsolicited e-mail; batch learning; e-mails; incremental learning; incremental spam detection algorithm; non incremental algorithms; training data; weighted majority voting; Accuracy; Algorithm design and analysis; Classification algorithms; Electronic mail; Machine learning algorithms; Training; Training data; Spam Detection; ensemble learning; incremental learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Artificial Intelligence and Signal Processing (AISP), 2011 International Symposium on
Conference_Location
Tehran
Print_ISBN
978-1-4244-9833-8
Type
conf
DOI
10.1109/AISP.2011.5960991
Filename
5960991
Link To Document