Al Moubayed, N. and Breckon, T.P. and Matthews, P.C. and McGough, A.S. (2016) 'SMS spam filtering using probabilistic topic modelling and Stacked Denoising Autoencoder.', in Artificial neural networks and machine learning – ICANN 2016 : 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016 ; proceedings. Part II. Cham: Springer, pp. 423-430. Lecture notes in computer science. (9886).
In This paper we present a novel approach to spam filtering and demonstrate its applicability with respect to SMS messages. Our approach requires minimum features engineering and a small set of labelled data samples. Features are extracted using topic modelling based on latent Dirichlet allocation, and then a comprehensive data model is created using a Stacked Denoising Autoencoder (SDA). Topic modelling summarises the data providing ease of use and high interpretability by visualising the topics using word clouds. Given that the SMS messages can be regarded as either spam (unwanted) or ham (wanted), the SDA is able to model the messages and accurately discriminate between the two classes without the need for a pre-labelled training set. The results are compared against the state-of-the-art spam detection algorithms with our proposed approach achieving over 97 % accuracy which compares favourably to the best reported algorithms presented in the literature.
|Item Type:||Book chapter|
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||http://dx.doi.org/10.1007/978-3-319-44781-0_50|
|Publisher statement:||The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-44781-0_50|
|Date accepted:||16 June 2016|
|Date deposited:||24 June 2016|
|Date of first online publication:||13 August 2016|
|Date first made open access:||13 August 2017|
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