Feature selection practice for unsupervised learning of credit card fraud detection

Hojin Lee, Dahee Choi, Habin Yim, Eunyoung Choi, Woong Go, Taejin Lee, In-Seok Kim, Kyung Ho Lee

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

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
Volume96
Issue number2
Publication statusPublished - 2018 Jan 1

Fingerprint

Fraud Detection
Unsupervised learning
Unsupervised Learning
Feature Selection
Feature extraction
Transactions
Predictability
Learning Process
Compliance
Increment
Target
Costs

Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Feature selection practice for unsupervised learning of credit card fraud detection. / Lee, Hojin; Choi, Dahee; Yim, Habin; Choi, Eunyoung; Go, Woong; Lee, Taejin; Kim, In-Seok; Lee, Kyung Ho.

In: Journal of Theoretical and Applied Information Technology, Vol. 96, No. 2, 01.01.2018, p. 408-417.

Research output: Contribution to journalArticle

Lee, Hojin ; Choi, Dahee ; Yim, Habin ; Choi, Eunyoung ; Go, Woong ; Lee, Taejin ; Kim, In-Seok ; Lee, Kyung Ho. / Feature selection practice for unsupervised learning of credit card fraud detection. In: Journal of Theoretical and Applied Information Technology. 2018 ; Vol. 96, No. 2. pp. 408-417.
@article{df40a0741acd49ce8cd30a0961c6f51c,
title = "Feature selection practice for unsupervised learning of credit card fraud detection",
abstract = "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.",
keywords = "Credit card fraud detection, Feature selection, Filtered algorithm, Ranked algorithm, Unsupervised learning",
author = "Hojin Lee and Dahee Choi and Habin Yim and Eunyoung Choi and Woong Go and Taejin Lee and In-Seok Kim and Lee, {Kyung Ho}",
year = "2018",
month = "1",
day = "1",
language = "English",
volume = "96",
pages = "408--417",
journal = "Journal of Theoretical and Applied Information Technology",
issn = "1992-8645",
publisher = "Asian Research Publishing Network (ARPN)",
number = "2",

}

TY - JOUR

T1 - Feature selection practice for unsupervised learning of credit card fraud detection

AU - Lee, Hojin

AU - Choi, Dahee

AU - Yim, Habin

AU - Choi, Eunyoung

AU - Go, Woong

AU - Lee, Taejin

AU - Kim, In-Seok

AU - Lee, Kyung Ho

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

KW - Credit card fraud detection

KW - Feature selection

KW - Filtered algorithm

KW - Ranked algorithm

KW - Unsupervised learning

UR - http://www.scopus.com/inward/record.url?scp=85041367135&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85041367135&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:85041367135

VL - 96

SP - 408

EP - 417

JO - Journal of Theoretical and Applied Information Technology

JF - Journal of Theoretical and Applied Information Technology

SN - 1992-8645

IS - 2

ER -