MDLText aplicado na Filtragem Automática de SPIM e SMS Spam

Autores

  • Renato Moraes Silva Universidade Estadual de Campinas http://orcid.org/0000-0001-6687-8981
  • 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)

Palavras-chave:

Aprendizado online, Navalha de Occam, Categorização de texto, Aprendizado de máquina

Resumo

A filtragem automática de spam em mensagens instantâneas e SMS é um problema desafiador, pois as mensagens são frequentemente curtas e repletas de ruídos, tais como gírias, expressões idiomáticas, símbolos, emoticons e abreviações, o que dificulta a extração de conhecimento e predição. Para enfrentar esse problema, neste artigo é avaliado um método de classificação de texto baseado no princípio da descrição mais simples, que é eficiente, rápido, escalável, multiclasse e possui aprendizado incremental. Experimentos realizados com uma base de dados real e pública, em cenários de aprendizado online e offline, indicam que o método proposto é promissor para a tarefa de detecção de spam em mensagens instantâneas e SMS.

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Biografia do Autor

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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Publicado

2018-05-21

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Silva, R. M., Almeida, T. A., & Yamakami, A. (2018). MDLText aplicado na Filtragem Automática de SPIM e SMS Spam. ISys - Brazilian Journal of Information Systems, 11(1), 103–132. Recuperado de https://seer.unirio.br/isys/article/view/6362

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