Balancing Data Within Incremental Semi-supervised Fuzzy Clustering for Credit Card Fraud Detection

DOI

10.2991/asum.k.210827.013How to use a DOI?

Keywords

Web Economy, Cyber Security, Credit Card Fraud Detection, Stream Data Mining, Semi-supervised learning, Re-sampling algorithms

Abstract

As the number of online financial transactions increases, the problem of credit card fraud detection has become quite urgent. Machine learning methods, including supervised and unsupervised approaches, have been proven to be effective to detect fraudulent activities. In our previous work presented at EUSFLAT2019 we proposed the use of an incremental semi-supervised fuzzy clustering that processes both labeled and unlabeled data as a stream to create a classification model for credit card fraud detection. However, we observed that the results of the method were affected by data unbalancement. Indeed credit card fraud data are highly imbalanced since the number of fraudulent activities is far less than the genuine ones. In this work, to deal with the high data unbalance, different resampling methods are investigated and their empirical comparison is reported.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Gabriella Casalino
AU  - Giovanna Castellano
AU  - Nicola Marvulli
PY  - 2021
DA  - 2021/08/30
TI  - Balancing Data Within Incremental Semi-supervised Fuzzy Clustering for Credit Card Fraud Detection
BT  - Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA), the 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and the 11th International Summer School on Aggregation Operators (AGOP)
PB  - Atlantis Press
SP  - 95
EP  - 102
SN  - 2589-6644
UR  - https://doi.org/10.2991/asum.k.210827.013
DO  - 10.2991/asum.k.210827.013
ID  - Casalino2021
ER  -