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A new semantic attribute deep learning with a linguistic attribute hierarchy for spam detection
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He, Hongmei, Watson, Tim, Maple, Carsten, Mehnen, Jorn and Ashutosh, Tiwari (2017) A new semantic attribute deep learning with a linguistic attribute hierarchy for spam detection. In: IJCNN 2017 : International Joint Conference on Neural Networks, Anchorage, Alaska, 14-19 May 2017 pp. 1-8. (Submitted)
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Official URL: http://ieeexplore.ieee.org/Xplore/home.jsp
Abstract
The massive increase of spam is posing a very serious threat to email and SMS, which have become an important means of communication. Not only do spams annoy users, but they also become a security threat. Machine learning techniques have been widely used for spam detection. In this paper, we propose another form of deep learning, a linguistic attribute hierarchy, embedded with linguistic decision trees, for spam detection, and examine the effect of semantic attributes on the spam detection, represented by the linguistic attribute hierarchy. A case study on the SMS message database from the UCI machine learning repository has shown that a linguistic attribute hierarchy embedded with linguistic decision trees provides a transparent approach to in-depth analysing attribute impact on spam detection. This approach can not only efficiently tackle ‘curse of dimensionality’ in spam detection with massive attributes, but also improve the performance of spam detection when the semantic attributes are constructed to a proper hierarchy.
Item Type: | Conference Item (Paper) | ||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||
Library of Congress Subject Headings (LCSH): | Spam filtering (Electronic mail), Text messages (Cell phone systems), Machine learning , Decision trees, Semantic computing | ||||
Publisher: | IEEE | ||||
Official Date: | 18 February 2017 | ||||
Dates: |
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Page Range: | pp. 1-8 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Submitted | ||||
Conference Paper Type: | Paper | ||||
Title of Event: | IJCNN 2017 : International Joint Conference on Neural Networks | ||||
Type of Event: | Conference | ||||
Location of Event: | Anchorage, Alaska | ||||
Date(s) of Event: | 14-19 May 2017 | ||||
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