TY - GEN
T1 - Credit Card Fraud Detection Based on MiniKM-SVMSMOTE-XGBoost Model
AU - Gu, Yanzhao
AU - Wei, Junhao
AU - Cheong, Ngai
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s).
PY - 2024/12/12
Y1 - 2024/12/12
N2 - In recent years, the problem of credit card fraud has become more acute with the digitisation of credit cards. For the high data volume, high dimensionality and extreme imbalance of credit card transaction data. This paper explores the application in the field of credit card fraud detection based on MiniBatchKMeans-SVMSMOTE-XGBoost model. Through combining clustering, oversampling and classification algorithms, an improved fraud detection method is proposed. The experimental results show that the model performs well in handling unbalanced data with high accuracy and generalisation ability.
AB - In recent years, the problem of credit card fraud has become more acute with the digitisation of credit cards. For the high data volume, high dimensionality and extreme imbalance of credit card transaction data. This paper explores the application in the field of credit card fraud detection based on MiniBatchKMeans-SVMSMOTE-XGBoost model. Through combining clustering, oversampling and classification algorithms, an improved fraud detection method is proposed. The experimental results show that the model performs well in handling unbalanced data with high accuracy and generalisation ability.
KW - Credit Card Fraud Detection
KW - Imbalance data
KW - MiniKM
KW - SVMSMOTE
KW - XGBoost
UR - https://www.scopus.com/pages/publications/105005829569
U2 - 10.1145/3697355.3697397
DO - 10.1145/3697355.3697397
M3 - Conference contribution
AN - SCOPUS:105005829569
T3 - ACM International Conference Proceeding Series
SP - 252
EP - 258
BT - BDIOT 2024 - 2024 8th International Conference on Big Data and Internet of Things
PB - Association for Computing Machinery
T2 - 2024 8th International Conference on Big Data and Internet of Things, BDIOT 2024
Y2 - 14 September 2024 through 16 September 2024
ER -