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 language | English |
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Pages (from-to) | 408-417 |
Number of pages | 10 |
Journal | Journal of Theoretical and Applied Information Technology |
Volume | 96 |
Issue number | 2 |
Publication status | Published - 2018 Jan 1 |
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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 journal › Article
}
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 -