Use of optimized Fuzzy C-Means clustering and supervised classifiers for automobile insurance fraud detection-Reference-Cited by-同舟云学术

Use of optimized Fuzzy C-Means clustering and supervised classifiers for automobile insurance fraud detection

Published:2020-06 Issue:5 Volume:32 Page:568-575
ISSN:1319-1578
Container-title:Journal of King Saud University - Computer and Information Sciences
language:en
Short-container-title:Journal of King Saud University - Computer and Information Sciences

Author:

Subudhi Sharmila,Panigrahi Suvasini

Publisher

Elsevier BV

Subject

General Computer Science

Reference35 articles.

1. Fraud detection system: a survey;Abdallah;J. Network Comput. Appl.,2016

2. Australia: Insurance, April 2016. Australia: Insurance fraud costs us 1.5 bln annually.http://www.insurancefraud.org/IFNS-detail.htm?key=22516 (accessed: 9.05.17).

3. A bayesian dichotomous model with asymmetric link for fraud in insurance;Bermúdez;Insurance: Math. Econ.,2008

4. Mean–variance optimal portfolios in the presence of a benchmark with applications to fraud detection;Bernard;Eur. J. Oper. Res.,2014

5. Fcm: The fuzzy c-means clustering algorithm;Bezdek;Comput. Geosci.,1984

Cited by 54 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Fraud risk assessment in car insurance using claims graph features in machine learning;Expert Systems with Applications;2024-10

2. Financial fraud detection through the application of machine learning techniques: a literature review;Humanities and Social Sciences Communications;2024-09-03

3. SSFCM-FWCW: Semi-Supervised Fuzzy C-Means method based on Feature-Weight and Cluster-Weight learning;Software Impacts;2024-09

4. Non-negative Sparse Matrix Factorization for Soft Clustering of Territory Risk Analysis;Annals of Data Science;2024-08-10

5. Enhancing fraud detection in auto insurance and credit card transactions: a novel approach integrating CNNs and machine learning algorithms;PeerJ Computer Science;2024-06-28