Credit card fraud detection by dynamic incremental semi-supervised fuzzy clustering

被引：0
|
作者：
Casalino, Gabriella [1 ]
Castellano, Giovanna [1 ]
Mencar, Corrado [1 ]
机构：
[1] Univ Bari Aldo Moro, Dept Comp Sci, Bari, Italy
来源：
PROCEEDINGS OF THE 11TH CONFERENCE OF THE EUROPEAN SOCIETY FOR FUZZY LOGIC AND TECHNOLOGY (EUSFLAT 2019) | 2019年 / 1卷
关键词：
Credit card fraud detection; Data stream classification; Semi-supervised fuzzy clustering; Incremental adaptive learning;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The problem of credit card fraud detection is approached by a semi-supervised classification task on a data stream. The DISSFCM algorithm is applied, which is based on Dynamic Incremental Semi-Supervised Fuzzy C-Means that processes data grouped in small-size chunks. Experimental results on a real-world dataset of credit card transactions show that DISSFCM has comparable results with some fully-supervised stream data classification methods, also in presence of a high percentage of unlabeled data.
引用
收藏
页码：198 / 204
页数：7
相关论文
共 50 条
  • [41] Fraud Detection in Credit Card and Application of VAT Clustering Algorithm: A Review
    Abdulrazaq, A. A.
    Abdulrazaq, M. B.
    Umoh, I. J.
    Adedokun, E. A.
    2019 2ND INTERNATIONAL CONFERENCE OF THE IEEE NIGERIA COMPUTER CHAPTER (NIGERIACOMPUTCONF), 2019, : 339 - 345
  • [42] Robust Dynamic Semi-supervised Picture Fuzzy Clustering with KL Divergence and Local Information
    Wu, Chengmao
    Zhang, Jiajia
    Huang, Congcong
    Guo, Xiaokang
    COGNITIVE COMPUTATION, 2022, 14 (03) : 970 - 988
  • [43] Robust Dynamic Semi-supervised Picture Fuzzy Clustering with KL Divergence and Local Information
    Chengmao Wu
    Jiajia Zhang
    Congcong Huang
    Xiaokang Guo
    Cognitive Computation, 2022, 14 : 970 - 988
  • [44] Electricity fraud detection using committee semi-supervised learning
    Viegas, Joaquim L.
    Cepeda, Nuno M.
    Vieira, Susana M.
    2018 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2018,
  • [45] A Semi-supervised Graph Attentive Network for Financial Fraud Detection
    Wang, Daixin
    Lin, Jianbin
    Cui, Peng
    Jia, Quanhui
    Wang, Zhen
    Fang, Yanming
    Yu, Quan
    Zhou, Jun
    Yang, Shuang
    Qi, Yuan
    2019 19TH IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM 2019), 2019, : 598 - 607
  • [46] Suspicious: a Resilient Semi-Supervised Framework for Graph Fraud Detection
    Giles, Bastien
    Jeudy, Baptiste
    Largeron, Christine
    Saboul, Damien
    2023 IEEE 35TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE, ICTAI, 2023, : 212 - 220
  • [47] Fraud Detection in Big Data using Supervised and Semi-supervised Learning Techniques
    Melo-Acosta, German E.
    Duitama-Munoz, Freddy
    Arias-Londono, Julian D.
    2017 IEEE COLOMBIAN CONFERENCE ON COMMUNICATIONS AND COMPUTING (COLCOM), 2017,
  • [48] Semi-Supervised Semantic Dynamic Text Clustering Algorithm
    Qian Z.-S.
    Huang R.-Z.
    Wei Q.
    Qin Y.-B.
    Chen Y.-P.
    Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China, 2019, 48 (06): : 803 - 808
  • [49] Semi-Supervised Outlier Detection with Only Positive and Unlabeled Data Based on Fuzzy Clustering
    Daneshpazhouh, Armin
    Sami, Ashkan
    INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS, 2015, 24 (03)
  • [50] Structural Damage Detection Based on Semi-supervised Fuzzy C-means Clustering
    Liu, Zhen
    Zhou, Qifeng
    Chi, Qijun
    Zhang, Yuanyuan
    Chen, Youling
    Qi, Sen
    2014 PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE & EDUCATION (ICCSE 2014), 2014, : 551 - 556
12345