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Apply Stacked Auto-Encoder to Spam Detection

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Advances in Swarm and Computational Intelligence (ICSI 2015)

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Abstract

In this paper, we apply Stacked Auto-encoder, one of the main types of deep networks, hot topic of machine learning recently, to spam detection and comprehensively compare its performance with other prevalent machine learning techniques those are commonly used in spam filtering. Experiments were conducted on five benchmark corpora, namely PU1, PU2, PU3, PUA and Enron-Spam. Accuracy and \(F_1\) measure are selected as the main criteria in analyzing and discussing the results. Experimental results demonstrate that Stacked Auto-encoder performs better than Naive Bayes, Support Vector Machine, Decision Tree, Boosting, Random Forest and traditional Artificial Neural Network both in accuracy and \(F_1\) measure, which endows deep learning with application in spam filtering in the real world.

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Author information

Authors and Affiliations

  1. Key Laboratory of Machine Perception (MOE), Peking University, Beijing, China

    Guyue Mi, Yang Gao & Ying Tan

  2. Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

    Guyue Mi, Yang Gao & Ying Tan

Authors
  1. Guyue Mi
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  2. Yang Gao
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  3. Ying Tan
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Corresponding author

Correspondence to Ying Tan .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Xi'ian Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  3. Universidade de Pernambuco, Recife, Brazil

    Fernando Buarque

  4. Instituto Politécnico Nacional, Mexico, Mexico

    Alexander Gelbukh

  5. Indian Statistical Institute, Kolkata, India

    Swagatam Das

  6. University of Pretoria, Pretoria, South Africa

    Andries Engelbrecht

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Mi, G., Gao, Y., Tan, Y. (2015). Apply Stacked Auto-Encoder to Spam Detection. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds) Advances in Swarm and Computational Intelligence. ICSI 2015. Lecture Notes in Computer Science(), vol 9141. Springer, Cham. https://doi.org/10.1007/978-3-319-20472-7_1

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  • DOI: https://doi.org/10.1007/978-3-319-20472-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20471-0

  • Online ISBN: 978-3-319-20472-7

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