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Heteroscedastic Discriminant Analysis Combined with Feature Selection for Credit Scoring

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  • Stąpor Katarzyna

    (Institute of Computer Science, Silesian University of Technology, Gliwice, Poland)

  • Smolarczyk Tomasz

    (Institute of Computer Science, Silesian University of Technology, Gliwice, Poland)

  • Fabian Piotr

    (Institute of Computer Science, Silesian University of Technology, Gliwice, Poland)

Abstract

Credit granting is a fundamental question and one of the most complex tasks that every credit institution is faced with. Typically, credit scoring databases are often large and characterized by redundant and irrelevant features. An effective classification model will objectively help managers instead of intuitive experience. This study proposes an approach for building a credit scoring model based on the combination of heteroscedastic extension (Loog, Duin, 2002) of classical Fisher Linear Discriminant Analysis (Fisher, 1936, Krzyśko, 1990) and a feature selection algorithm that retains sufficient information for classification purpose. We have tested five feature subset selection algorithms: two filters and three wrappers. To evaluate the accuracy of the proposed credit scoring model and to compare it with the existing approaches we have used the German credit data set from the study (Chen, Li, 2010). The results of our study suggest that the proposed hybrid approach is an effective and promising method for building credit scoring models.

Suggested Citation

  • Stąpor Katarzyna & Smolarczyk Tomasz & Fabian Piotr, 2016. "Heteroscedastic Discriminant Analysis Combined with Feature Selection for Credit Scoring," Statistics in Transition New Series, Statistics Poland, vol. 17(2), pages 265-280, June.
    • Handle: RePEc:vrs:stintr:v:17:y:2016:i:2:p:265-280:n:13
    DOI: 10.21307/stattrans-2016-018
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    References listed on IDEAS

    as
    1. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
    2. Pacheco, Joaquin & Casado, Silvia & Nunez, Laura & Gomez, Olga, 2006. "Analysis of new variable selection methods for discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1463-1478, December.
    3. Crook, Jonathan N. & Edelman, David B. & Thomas, Lyn C., 2007. "Recent developments in consumer credit risk assessment," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1447-1465, December.
    4. L C Thomas & R W Oliver & D J Hand, 2005. "A survey of the issues in consumer credit modelling research," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(9), pages 1006-1015, September.
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