Credit Card Fraud Detection: An Improved Strategy for High Recall Using KNN, LDA, and Linear Regression

Jiwon Chung, Kyungho Lee

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Efficiently and accurately identifying fraudulent credit card transactions has emerged as a significant global concern along with the growth of electronic commerce and the proliferation of Internet of Things (IoT) devices. In this regard, this paper proposes an improved algorithm for highly sensitive credit card fraud detection. Our approach leverages three machine learning models: K-nearest neighbor, linear discriminant analysis, and linear regression. Subsequently, we apply additional conditional statements, such as “IF” and “THEN”, and operators, such as “>“ and “<“, to the results. The features extracted using this proposed strategy achieved a recall of 1.0000, 0.9701, 1.0000, and 0.9362 across the four tested fraud datasets. Consequently, this methodology outperforms other approaches employing single machine learning models in terms of recall.

Original languageEnglish
Article number7788
JournalSensors
Volume23
Issue number18
DOIs
Publication statusPublished - 2023 Sept

Bibliographical note

Publisher Copyright:
© 2023 by the authors.

Keywords

  • KNN
  • LDA
  • credit card fraud detection
  • linear regression
  • recall analysis
  • sensitivity analysis
  • true positive rate analysis

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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