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An incremental spam detection algorithm

Ghanbari, E ; Sharif University of Technology | 2011

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  1. Type of Document: Article
  2. DOI: 10.1109/AISP.2011.5960991
  3. Publisher: 2011
  4. Abstract:
  5. 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
  6. Keywords:
  7. Batch learning ; Combining classifiers ; Ensemble learning ; Incremental algorithm ; Incremental learning ; Majority voting ; Spam detection ; Training data ; Artificial intelligence ; Internet ; Signal detection ; Learning algorithms
  8. Source: 2011 International Symposium on Artificial Intelligence and Signal Processing, AISP 2011, 15 June 2011 through 16 June 2011 ; June , 2011 , Pages 31-36 ; 9781424498345 (ISBN)
  9. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5960991