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

DOI

10.2991/eusflat-19.2019.30How to use a DOI?

Keywords

Credit card fraud detection Data stream classification Semi-supervised fuzzy clustering Incremental adaptive learning

Abstract

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.

Copyright

© 2019, 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  - Corrado Mencar
PY  - 2019/08
DA  - 2019/08
TI  - Credit card fraud detection by dynamic incremental semi-supervised fuzzy clustering
BT  - Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
PB  - Atlantis Press
SP  - 198
EP  - 204
SN  - 2589-6644
UR  - https://doi.org/10.2991/eusflat-19.2019.30
DO  - 10.2991/eusflat-19.2019.30
ID  - Casalino2019/08
ER  -