Title :
An incremental spam detection algorithm
Author :
Ghanbari, Elham ; Beigy, Hamid
Author_Institution :
Dept. of Comput. Eng., Sharif Univ. of Technol., Tehran, Iran
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;
Conference_Titel :
Artificial Intelligence and Signal Processing (AISP), 2011 International Symposium on
Conference_Location :
Tehran
Print_ISBN :
978-1-4244-9833-8
DOI :
10.1109/AISP.2011.5960991
