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Cluster Computing

, Volume 22, Supplement 6, pp 13861–13866 | Cite as

Credit card fraud forecasting model based on clustering analysis and integrated support vector machine

  • Chunhua Wang
  • Dong HanEmail author
Article

Abstract

At present, with the popularization of credit cards, credit card fraud increases gradually. Based on this, this paper designs a credit card fraud prediction model based on cluster analysis and integrated support vector machine using computer technology. First of all, adjust and reduce the Unbalanced state based on K-means clustering analysis combined with more than half of the random samples. Secondly, the use of the idea of integrated learning to further deal with the Unbalanced state of the data and increase classifier’s awareness of minorities. Finally, we tested the algorithm, and the result showed that the proposed algorithm effectively reduced the cost of accidental injury, which provides a great possibility for the card issuer to effectively reduce the economic losses caused by credit card fraud, which has laid a good theoretical basis and foundation for practical application.

Keywords

Cluster analysis Integration improvement Support vector machine Credit card fraud prediction model 

Notes

Funding

Funding were provided by Key Science-Technology Project of Henan Province (Grant No. 172102210117) and Key Science-Technology Project of Zhumadian City, Henan Province (Grant No. 17135).

References

  1. 1.
    Tanaka, Y., Takahashi, M.: Dynamic time warping-based cluster analysis and support vector machine-based prediction of solar irradiance at multi-points in a wide area [J]. Procedia Comput. Sci. 2(16), 210–215 (2016)Google Scholar
  2. 2.
    Zareapoor, M., Shamsolmoali, P.: Application of credit card fraud detection: based on bagging ensemble classifier [J]. Procedia Comput. Sci. 48(1), 679–685 (2015)CrossRefGoogle Scholar
  3. 3.
    Zhang, L., Rao, K., Wang, R., et al.: Risk prediction model based on improved adaboost method for cloud usersse [J]. Open Cybern. Syst. J. 9(1), 44–49 (2015)CrossRefGoogle Scholar
  4. 4.
    Bae, K.Y., Han, S.J., Dan, K.S.: Hourly solar irradiance prediction based on support vector machine and its error analysis [J]. IEEE Trans. Power Syst. PP(99), 1 (2017)Google Scholar
  5. 5.
    Huang, X., Fan, X., Chen, X., et al.: Bed permeability state prediction model of sintering process based on data mining technology [J]. ISIJ Int. 56(12), 2113–2117 (2016)CrossRefGoogle Scholar
  6. 6.
    Guo, S., Yuan, D., Zhang, R., et al.: Prediction of human promoter with least square support vector machine based on the kernel locality preserving projection [J]. Chemometr. Intell. Lab. Syst. 158, 69–79 (2016)CrossRefGoogle Scholar
  7. 7.
    Subudhi, S., Panigrahi, S.: Use of fuzzy clustering and support vector machine for detecting fraud in mobile telecommunication networks [J]. Int. J. Secur. Netw. 11(1/2), 3 (2016)CrossRefGoogle Scholar
  8. 8.
    Yu, B., Gao, J.R., Ding, D., et al.: Accurate lithography hotspot detection based on principal component analysis-support vector machine classifier with hierarchical data clustering [J]. J. Micro/Nanolithogr. MEMS MOEMS 14(1), 2006–2021 (2015)Google Scholar
  9. 9.
    Ahmed, M., Mahmood, A.N.: Novel approach for network traffic pattern analysis using clustering-based collective anomaly detection [J]. Ann. Data Sci. 2(1), 1–20 (2015)CrossRefGoogle Scholar
  10. 10.
    García, V., Marqués, A.I., Sánchez, J.S.: An insight into the experimental design for credit risk and corporate bankruptcy prediction systems [J]. J. Intell. Inf. Syst. 44(1), 159–189 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of AnimationHuanghuai UniversityZhumadianChina

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