NDLI: Adaptive spam filtering using dynamic feature space

Content Provider	IEEE Xplore Digital Library
Author	Yan Zhou Mulekar, M.S. Nerellapalli, P.
Copyright Year	2005
Description	Author affiliation: Sch. of CIS, South Alabama Univ., Mobile, AL (Yan Zhou)
Abstract	Unsolicited bulk e-mail, also known as spam, has been an increasing problem for the e-mail society. This paper presents a new spam filtering strategy that 1) uses a practical entropy coding technique, Huffman coding, to dynamically encode the feature space of e-mail collections over time and, 2) applies an online algorithm to adaptively enhance the learned spam concept as new e-mail data becomes available. The contributions of this work include a highly efficient spam filtering algorithm in which the input space is radically reduced to a single-dimension input vector, and an adaptive learning technique that is robust to vocabulary change, concept drifting and skewed data distribution. We compare our technique to several existing off-line learning techniques including support vector machine, naive Bayes, k-nearest neighbor, C4.5 decision tree, RBFNetwork, boosted decision tree and stacking, and demonstrate the effectiveness of our technique by presenting the experimental results on the e-mail data that is publicly available
Starting Page	8
Ending Page	309
File Size	305731
Page Count	302
File Format	PDF
ISBN	0769524885
ISSN	10823409
DOI	10.1109/ICTAI.2005.28
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-11-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptive filters Electronic mail Filtering algorithms Decision trees Entropy coding Huffman coding Robustness Vocabulary Machine learning Support vector machines
Content Type	Text
Resource Type	Article

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