A new semantic attribute deep learning with a linguistic attribute hierarchy for spam detection

Hongmei He, Tim Watson, Carsten Maple, Jörn Mehnen, Ashutosh Tiwari

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

14 Citations (Scopus)
16 Downloads (Pure)


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.
Original languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks (IJCNN)
Place of PublicationPiscataway, NJ
Number of pages8
ISBN (Electronic)9781509061822
Publication statusPublished - 3 Jul 2017


  • deep learning
  • spam detection


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