JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2017, Vol. 52 ›› Issue (7): 73-79.doi: 10.6040/j.issn.1671-9352.1.2016.041

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Spam messages identification based on multi-feature fusion

LI Run-chuan1,2,3, ZAN Hong-ying1, SHEN Sheng-ya4, BI Yin-long1, ZHANG Zhong-jun5   

  1. 1. School of Information Engineering, Zhengzhou University, Zhengzhou 450000, Henan, China;
    2. Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450000, Henan, China;
    3.Research Institute of Industrial Technology, Zhengzhou University, Zhengzhou 450000, Henan, China;
    4. School of Foreign Language, Zhengzhou University, Zhengzhou 450000, Henan, China;
    5. School of Computer Science and Technology of Zhoukou Normal University, Zhoukou 466001, Henan, China
  • Received:2016-11-25 Online:2017-07-20 Published:2017-07-07

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Abstract: Spam message has increasingly become a serious problem affecting peoples daily live. the informative texts are short and sparse, especially the spam message, in order to avoid filtering mechanism, its structure and content is not always standardized so that the traditional text feature extraction method does not fully apply to text classification. This paper extract the feature item from the structure and semantics of two angles of short message, establish semantic feature list and use multi-feature fusion method to quantitatively express SMS text. According to noise and data imbalance problem exists in message, this paper compares the performance differences of NB, SVM, DT, LR, MLP and RF. The experiment shows that the RF classification algorithm can effectively reduce the impact of noise interference and data imbalance. Through the experiments on the data set which provided by Spam Message Based on Text Content Recognition in CCF 2015 China Creative Competition proved that our method works well.

Key words: data imbalance, multi-feature fusion, spam message, random forest

CLC Number: 

  • TP391
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