Feature engineering strategies for credit card fraud detection
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- 作者：Alejandro Correa Bahnsen ; al.bahnsen@gmail.com" class="auth_mail" title="E-mail the corresponding author ; Djamila Aouada djamila.aouada@uni.lu" class="auth_mail" title="E-mail the corresponding author ; Aleksandar Stojanovic aleksandar.stojanovic@rwth-aachen.de" class="auth_mail" title="E-mail the corresponding author ; Bjö ; rn Ottersten bjorn.ottersten@uni.lu" class="auth_mail" title="E-mail the corresponding author
- 关键词：Cost-sensitive learning ; Fraud detection ; Preprocessing ; Von Mises distribution
- 刊名：Expert Systems with Applications
- 出版年：2016
- 出版时间：1 June 2016
- 年：2016
- 卷：51
- 期：Complete
- 页码：134-142
- 全文大小：1248 K
文摘
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Credit card fraud detection evaluation measure.
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Each example is assumed to have different financial cost.
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Transaction aggregation strategy for predicting fraud.
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Periodic features using the von Mises distribution.
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Code is open source and available at albahnsen.com/CostSensitiveClassification.