Skip to main content
SpringerLink
Log in

Community-Based Feature Selection for Credit Card Default Prediction

  • Conference paper
  • First Online:
Book cover Complex Networks & Their Applications VI (COMPLEX NETWORKS 2017)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 689))

Included in the following conference series:

Abstract

The prediction of credit card default is a critical issue in business and so has been attracting more and more attention. In this paper, we focus on the research of credit card default prediction in an unconventional way. We firstly study the consumption behavior of credit card holders, and uncover an interesting pattern that the features (each feature represents one dimension of the consumption behavior) cluster into different groups. With the aim of exploring the effect of the observed pattern on the task of credit card default prediction, we further propose a feature selection algorithm. Finally, we test the proposed algorithm and four existing feature selection algorithms on four prediction models over the real dataset of credit card consumption. Experimental results show that the proposed algorithm gives the overall superior performance and spends less running time in most cases; this demonstrates the potential application of the observed pattern.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Community is a subset of nodes which are densely connected with each other while being separated well with other parts in the network. Community has the same meaning with cluster, without consideration of the representation of data. We use community, cluster, and group changeably in this paper.

References

  1. Ajay, A., Venkatesh, A., Gracia, S., et al.: Prediction of credit-card defaulters: a comparative study on performance of classifiers. Int. J. Comput. Appl. 145(7), 36–41 (2016)

    Google Scholar 

  2. Leow, M., Crook, J.: A new Mixture model for the estimation of credit card Exposure at Default. Eur. J. Oper. Res. 249(2), 487–497 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  3. Sun, S.H., Jin, Z.: Estimating credit risk parameters using ensemble learning methods: an empirical study on loss given default. J. Credit Risk (August 9, 2016). (Forthcoming)

    Google Scholar 

  4. Bermingham, M.L., Pongwong, R., Spiliopoulou, A., et al. Application of high-dimensional feature selection: evaluation for genomic prediction in man. Sci. Rep. 10312–10312 (2015)

    Google Scholar 

  5. Li J., Cheng K., Wang S., et al.: Feature Selection: A Data Perspective (2016). arXiv:1601.07996

  6. Alelyani, S., Tang, J., Liu, H.: Feature selection for clustering: a review. Encycl. Database Syst. 21(3), 110–121 (2016)

    Google Scholar 

  7. Abdou, H.A., Tsafack, M., Ntim, C.G., et al.: Predicting creditworthiness in retail banking with limited scoring data. Knowl Based Syst 103(1), 89–103 (2016)

    Article  Google Scholar 

  8. Wang, H., Xu, Q., Zhou, L., et al.: Large Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble. PLOS ONE 10(2), e0117844 (2015)

    Article  Google Scholar 

  9. Hon, P.S., Bellotti, T.: Models and forecasts of credit card balance. Eur. J. Oper. Res. 249(2), 498–505 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  10. Evangelista, R.D., Artes R.: Using multi-state markov models to identify credit card risk. Production 26(2), The Scientific Electronic Library Online (2016)

    Google Scholar 

  11. Yang, J., Leskovec, J.: Structure and overlaps of ground-truth communities in networks. ACM Trans. Intell. Syst. Technol. 5(2), 26 (2014)

    Article  Google Scholar 

  12. Hu, Y., Yang, B., Wong, H.: A weighted local view method based on observation over ground truth for community detection. Inf. Sci. 355–356, 37–57 (2016)

    Article  Google Scholar 

  13. Hu, Y., Yang, B.: Characterizing the structure of large real networks to improve community detection. Neural Comput. Appl. 28(8), 2363–2363 (2017)

    Article  Google Scholar 

  14. Nie, G., Wang, G., Zhang, P., et al.: Finding the hidden pattern of credit card holder’s churn: a case of China. In: International Conference on Computational Science (ICCS 2009), pp. 561–569. Springer, Berlin, Heidelberg (2009)

    Google Scholar 

  15. Zhao, B., Wang, W., Xue, G., et al.: An empirical analysis on temporal pattern of credit card trade. In: International Conference in Swarm Intelligence, pp. 63–70. Springer, Cham (2015)

    Google Scholar 

  16. Zhang, C., Kumar, A., Ré, C.: Materialization optimizations for feature selection workloads. ACM Trans. Database Syst. 41(1), 2 (2016)

    Article  MathSciNet  Google Scholar 

  17. Kung, S.Y., Mak, M.W.: Chapter 1: Feature Selection for genomic and proteomic datamining. Machine Learning in Bioinformatics. Wiley, Hoboken, NJ, USA (2009)

    Google Scholar 

  18. Boln-Canedo, V., Snchez-Maroo, N., Alonso-Betanzos, A.: Feature selection for high-dimensional data. Springer International Publishing (2015)

    Google Scholar 

  19. Asir Antony Gnana Singh, D., Appavu alias Balamurugan, S., Jebamalar Leavline, E.: Literature review on feature selection methods for high-dimensional data. Methods 136(1) (2016)

    Google Scholar 

  20. Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L:. Feature Extraction: Foundations and Applications. Springer (2006)

    Google Scholar 

  21. Tallón-Ballesteros, A.J., Riquelme, J.C., Ruiz, R.: Merging subsets of attributes to improve a hybrid consistency-based filter: a case of study in product unit neural networks. Connect. Sci. 28(3), 242–257 (2016)

    Article  Google Scholar 

  22. Peng, H., Ding, C., Long, F.: Minimum redundancy-maximum relevance feature selection and its applications. Feature Selection (2015)

    Google Scholar 

  23. Ruiz, R., Riquelme, J.C., Aguilar-Ruiz, J.S.: Incremental wrapper-based gene selection from microarray data for cancer classification. Pattern Recognit. 39(12), 2383–2392 (2006)

    Article  Google Scholar 

  24. Ma, L., Li, M., Gao, Y., et al.: A novel wrapper approach for feature selection in object-based image classification using polygon-based cross-validation. IEEE Geosci. Remote Sens. Lett. 99, 1–5 (2017)

    Google Scholar 

  25. Mejía-Lavalle, M., Sucar, E., Arroyo, G.: Feature selection with a perceptron neural networks. In: International Workshop on Feature Selection for Data Mining, pp. 131–135 (2006)

    Google Scholar 

  26. Lin, X., Yang, F., Zhou, L., et al.: A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information. J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 910(23), 149–155 (2012)

    Article  Google Scholar 

  27. Fu, H., Xiao, Z., Dellandréa, E., et al.: Image categorization using ESFS: a new embedded feature selection method based on SFS. In: International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 288–299. Bordeaux, France (2009)

    Google Scholar 

  28. Butterworth, R., Piatetskyshapiro, G., Simovici, D.A., et al.: On feature selection through clustering. In: 5th International Conference on Data Mining, pp. 581–584 (2005)

    Google Scholar 

  29. Zhou, X., Hu, Y., Guo, L., et al.: Text categorization based on clustering feature selection. Procedia Comput. Sci. 31, 398–405 (2014)

    Article  Google Scholar 

  30. Han, D., Kim, J.: Unsupervised simultaneous orthogonal basis clustering feature selection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5016–5023 (2015)

    Google Scholar 

  31. Pearson, K.: Note on regression and inheritance in the case of two parents. Proc. R. Soc. Lond. 58, 240–242 (1895)

    Article  Google Scholar 

  32. Prion, S., Haerling, K.A.: Making sense of methods and measurement: pearson product-moment correlation coefficient. Clin. Simul. Nurs. 10(11), 587–588 (2014)

    Article  Google Scholar 

  33. Sedgwick, P.: Spearman’s rank correlation coefficient. BMJ (2014)

    Google Scholar 

  34. Kim, T., Wright, S.: PMU placement for line outage identification via multinomial logistic regression. IEEE Trans. Smart Grid (2016)

    Google Scholar 

  35. Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Mach. Learn. 37(3), 297–336 (1999)

    Article  MATH  Google Scholar 

  36. Wang, W., Lin, W., Zhang, R., et al.: Research on human face location based on Adaboost and convolutional neural network. In: IEEE 2nd International Conference on Cloud Computing and Big Data Analysis (2017)

    Google Scholar 

  37. Byun, H., Lee, S.: Applications of support vector machines for pattern recognition: a survey. Lecture Notes in Computer Science (2002)

    Google Scholar 

  38. Deng, H., Runger, G.: Feature selection via regularized trees. Int. Jt. Conf. Neural Netw. (2015)

    Google Scholar 

  39. Liu, H., Motoda, H., Yu, L.: A Selective Sampling Approach to Active Feature Selection. Elsevier Science Publishers Ltd. (2015)

    Google Scholar 

  40. Sharma, A., Imoto, S., Miyano, S.: A Top-r feature selection algorithm for microarray gene expression data. IEEE/ACM Trans. Comput. Biol. Bioinform. 9(3), 754–764 (2015)

    Google Scholar 

  41. Arunkumar, C., Ramakrishnan, S.: A hybrid approach to feature selection using correlation coefficient and fuzzy rough quick reduct algorithm applied to cancer microarray data. Int. Conf. Intell. Syst. Control (2016)

    Google Scholar 

  42. Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006)

    Article  MathSciNet  Google Scholar 

  43. Davis, J., Goadrich, M.: The relationship between Precision-Recall and ROC curves. In: 23rd International Conference on Machine Learning, pp. 233–240. ACM, Pennsylvania, USA (2006)

    Google Scholar 

Download references

Acknowledgments

This work is supported by discipline construction project of modern information detection and intelligent processing technology and cultivating program of excellent innovation team of Chengdu University of Technology.

Author information

Authors and Affiliations

  1. Chengdu University of Technology, Chengdu, China

    Qiucheng Wang, Yanmei Hu & Jun Li

Authors
  1. Qiucheng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanmei Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yanmei Hu .

Editor information

Editors and Affiliations

  1. University of Lyon 2, Lyon, France

    Chantal Cherifi

  2. University of Burgundy, Dijon, France

    Hocine Cherifi

  3. École Normale Supérieure de Lyon, Lyon, France

    Márton Karsai

  4. University College London, London, United Kingdom

    Mirco Musolesi

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, Q., Hu, Y., Li, J. (2018). Community-Based Feature Selection for Credit Card Default Prediction. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-72150-7_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72149-1

  • Online ISBN: 978-3-319-72150-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation