IDEAS home Printed from https://ideas.repec.org/a/taf/tsysxx/v45y2014i10p2213-2222.html
   My bibliography  Save this article

An application of locally linear model tree algorithm with combination of feature selection in credit scoring

Author

Listed:
  • Mohammad Siami
  • Mohammad Reza Gholamian
  • Javad Basiri

Abstract

Nowadays, credit scoring is one of the most important topics in the banking sector. Credit scoring models have been widely used to facilitate the process of credit assessing. In this paper, an application of the locally linear model tree algorithm (LOLIMOT) was experimented to evaluate the superiority of its performance to predict the customer's credit status. The algorithm is improved with an aim of adjustment by credit scoring domain by means of data fusion and feature selection techniques. Two real world credit data sets – Australian and German – from UCI machine learning database were selected to demonstrate the performance of our new classifier. The analytical results indicate that the improved LOLIMOT significantly increase the prediction accuracy.

Suggested Citation

  • Mohammad Siami & Mohammad Reza Gholamian & Javad Basiri, 2014. "An application of locally linear model tree algorithm with combination of feature selection in credit scoring," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(10), pages 2213-2222, October.
    • Handle: RePEc:taf:tsysxx:v:45:y:2014:i:10:p:2213-2222
    DOI: 10.1080/00207721.2013.767395
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207721.2013.767395
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207721.2013.767395?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hoffmann, F. & Baesens, B. & Mues, C. & Van Gestel, T. & Vanthienen, J., 2007. "Inferring descriptive and approximate fuzzy rules for credit scoring using evolutionary algorithms," European Journal of Operational Research, Elsevier, vol. 177(1), pages 540-555, February.
    2. Crone, Sven F. & Finlay, Steven, 2012. "Instance sampling in credit scoring: An empirical study of sample size and balancing," International Journal of Forecasting, Elsevier, vol. 28(1), pages 224-238.
    3. A. C. Antonakis & M. E. Sfakianakis, 2009. "Assessing naive Bayes as a method for screening credit applicants," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(5), pages 537-545.
    4. Lee, Tian-Shyug & Chiu, Chih-Chou & Chou, Yu-Chao & Lu, Chi-Jie, 2006. "Mining the customer credit using classification and regression tree and multivariate adaptive regression splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 1113-1130, February.
    5. Frydman, Halina & Altman, Edward I & Kao, Duen-Li, 1985. "Introducing Recursive Partitioning for Financial Classification: The Case of Financial Distress," Journal of Finance, American Finance Association, vol. 40(1), pages 269-291, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nadia Ayed & Khemaies Bougatef, 2024. "Performance Assessment of Logistic Regression (LR), Artificial Neural Network (ANN), Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy System (ANFIS) in Predicting Default Probability: The Case of," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1803-1835, September.
    2. Casado Yusta, Silvia & Nœ–ez Letamendía, Laura & Pacheco Bonrostro, Joaqu’n Antonio, 2018. "Predicting Corporate Failure: The GRASP-LOGIT Model || Predicci—n de la quiebra empresarial: el modelo GRASP-LOGIT," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 294-314, Diciembre.
    3. Natalia Nehrebecka, 2016. "Approach to the assessment of credit risk for non-financial corporations. Evidence from Poland," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Combining micro and macro data for financial stability analysis, volume 41, Bank for International Settlements.
    4. Nyitrai, Tamás & Virág, Miklós, 2019. "The effects of handling outliers on the performance of bankruptcy prediction models," Socio-Economic Planning Sciences, Elsevier, vol. 67(C), pages 34-42.
    5. Runchi Zhang & Zhiyi Qiu, 2020. "Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-35, June.
    6. Kim, Soo Y. & Upneja, Arun, 2014. "Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models," Economic Modelling, Elsevier, vol. 36(C), pages 354-362.
    7. Suzan Hol, 2006. "The influence of the business cycle on bankruptcy probability," Discussion Papers 466, Statistics Norway, Research Department.
    8. LaDue, Eddy L. & Miller, Lynn H. & Kwiatkowski, Joseph H., 1990. "Investment Behavior And Energy Conservation," Northeastern Journal of Agricultural and Resource Economics, Northeastern Agricultural and Resource Economics Association, vol. 19(2), pages 1-10, October.
    9. Maria H. Kim & Graham Partington, 2015. "Dynamic forecasts of financial distress of Australian firms," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 135-160, February.
    10. Pablo de Llano Monelos & Manuel Rodríguez López & Carlos Piñeiro Sánchez, 2013. "Bankruptcy Prediction Models in Galician companies. Application of Parametric Methodologies and Artificial Intelligence," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(1), pages 117-136.
    11. Catherine Refait, 2004. "La prévision de la faillite fondée sur l’analyse financière de l’entreprise : un état des lieux," Économie et Prévision, Programme National Persée, vol. 162(1), pages 129-147.
    12. Ruey-Ching Hwang & K. F. Cheng & Jack C. Lee, 2007. "A semiparametric method for predicting bankruptcy," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(5), pages 317-342.
    13. Sueyoshi, Toshiyuki, 2006. "DEA-Discriminant Analysis: Methodological comparison among eight discriminant analysis approaches," European Journal of Operational Research, Elsevier, vol. 169(1), pages 247-272, February.
    14. Foo See Liang & Shaak Pathak, 2019. "Understanding the Connection of Performance and Z-Scores for Manufacturing Firms in South Korea," Journal of Asian Development, Macrothink Institute, vol. 5(3), pages 37-46, November.
    15. repec:zbw:bofrdp:2009_035 is not listed on IDEAS
    16. S. Balcaen & H. Ooghe, 2004. "Alternative methodologies in studies on business failure: do they produce better results than the classical statistical methods?," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 04/249, Ghent University, Faculty of Economics and Business Administration.
    17. Kattan, Michael W. & Cooper, Randolph B., 2000. "A simulation of factors affecting machine learning techniques: an examination of partitioning and class proportions," Omega, Elsevier, vol. 28(5), pages 501-512, October.
    18. Lkhagvadorj Munkhdalai & Tsendsuren Munkhdalai & Oyun-Erdene Namsrai & Jong Yun Lee & Keun Ho Ryu, 2019. "An Empirical Comparison of Machine-Learning Methods on Bank Client Credit Assessments," Sustainability, MDPI, vol. 11(3), pages 1-23, January.
    19. Demyanyk, Yuliya & Hasan, Iftekhar, 2009. "Financial crises and bank failures: a review of prediction methods," Bank of Finland Research Discussion Papers 35/2009, Bank of Finland.
    20. Novak, Michael P. & LaDue, Eddy L., 1999. "Application Of Recursive Partitioning To Agricultural Credit Scoring," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 31(1), pages 1-14, April.
    21. Ha-Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," EconomiX Working Papers 2015-1, University of Paris Nanterre, EconomiX.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:tsysxx:v:45:y:2014:i:10:p:2213-2222. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TSYS20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.