Feature selection practice for unsupervised learning of credit card fraud detection

Hojin Lee, Dahee Choi, Habin Yim, Eunyoung Choi, Woong Go, Taejin Lee, Insuk Kim, Kyungho Lee

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Unsupervised Learning processes the massive data and discover the underlying patterns, even though explicit target values are nonexistent. Achieving high predictability for Unsupervised Learning, we practiced to select most influenced feature related to fraud detect system among numerous data. Financial transactions are provided through various channels. On this account, selection of new feature brings increment either on time and cost. In this paper, we practiced the various Feature Selection to detect abnormal transactions exploiting Unsupervised Learning. Here, we select proper features by valuing weight on various Feature Selection Algorithms. The efficiency and accuracy of Feature Selection we practiced are demonstrated by credit card data set. Therefore, it provides rapid response in compliance with feature variance and guide to efficient feature selection.

Original languageEnglish
Pages (from-to)408-417
Number of pages10
JournalJournal of Theoretical and Applied Information Technology
Issue number2
Publication statusPublished - 2018 Jan


  • Credit card fraud detection
  • Feature selection
  • Filtered algorithm
  • Ranked algorithm
  • Unsupervised learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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