MDLText applied to automatic filtering of SPIM and SMS Spam


  • Renato Moraes Silva Universidade Estadual de Campinas
  • Tiago A. Almeida Departamento de Computação (DComp) / Universidade Federal de São Carlos (UFSCar)
  • Akebo Yamakami Faculdade de Engenharia Elétrica e de Computação (FEEC) / Universidade Estadual de Campinas (UNICAMP)



Online learning, Occam’s razor, Text categorization, Machine learning


Spam filtering in online instant messages and SMS is a challenging problem nowadays. It is because the messages are often very short and rife with slangs, idioms, symbols, emoticons, and abbreviations which hamper predicting and knowledge discovering. In order to face this problem, we evaluated a simple, fast, scalable, multiclass, and online text classification method based on the minimum description length principle. We conducted experiments using a real and public dataset, which demonstrate that our method is effective on instant messaging and SMS spam filtering in both online and offline learning contexts.


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Author Biography

Renato Moraes Silva, Universidade Estadual de Campinas

Departamento de Sistemas e Energia (DSE) / Faculdade de Engenharia Elétrica e Computação (FEEC) / Universidade Estadual de Campinas - UNICAMP


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How to Cite

Silva, R. M., Almeida, T. A., & Yamakami, A. (2018). MDLText applied to automatic filtering of SPIM and SMS Spam. ISys - Brazilian Journal of Information Systems, 11(1), 103–132.



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