Multiclass SMS message categorization: Beyond spam binary classification

Fatia Kusuma Dewi, Mgs M.Rizqi Fadhlurrahman, Mohamad Dwiyan Rahmanianto, Rahmad Mahendra

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

SMS spam has been growing since mobile phone usage increases. Past researches on SMS spam detection only classified SMS into two categories, spam and not spam. The binary classification of SMS spam prevents the user from seeing the spam messages that they do not really hate, e.g. an advertisement from their favorite product In this paper, we propose multi-class classification of SMS into: regular, info, ads, and fraud. We use content-based (top-N unigram) as well as non-content based features. The result shows that the best accuracy is achieved by logistic regression that is 97.5 % accuracy with configuration of normalization preprocess and 4096 top-N unigram features.

Original languageEnglish
Title of host publication2017 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages210-215
Number of pages6
ISBN (Electronic)9781538631720
DOIs
Publication statusPublished - 4 May 2018
Event9th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017 - Jakarta, Indonesia
Duration: 28 Oct 201729 Oct 2017

Publication series

Name2017 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017
Volume2018-January

Conference

Conference9th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017
Country/TerritoryIndonesia
CityJakarta
Period28/10/1729/10/17

Keywords

  • Classification
  • Mobile SMS
  • Multiclass
  • Spam

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