IDEAS home Printed from https://ideas.repec.org/p/vnm/wpdman/18.html
   My bibliography  Save this paper

An evolutionary approach to preference disaggregation in a MURAME-based credit scoring problem

Author

Listed:
  • Marco Corazza

    (Department of Economics, Università Ca' Foscari Venezia)

  • Stefania Funari

    (Department of Management, Università Ca' Foscari Venezia)

  • Riccardo Gusso

    (Department of Economics, Università Ca' Foscari Venezia)

Abstract

In this paper we use an evolutionary approach in order to infer the values of the parameters (weights of criteria, preference, indifference and veto thresholds) for developing the multicriteria method MURAME. According to the logic of preference disaggregation, the problem consists in finding the parameters that minimize the inconsistency between the model obtained with those parameters and that one connected with a given reference set of decisions revealed by the decision maker; in particular, two kinds of functions are considered in this analysis, representing a measure of the model inconsistency compared to the actual preferential system. In order to find a numerical solution of the mathematical programming problem involved, we adopt an evolutionary algorithm based on the Particle Swarm Optimization (PSO) method, which is an iterative heuristics grounded on swarm intelligence. The proposed approach is finally applied to a creditworthiness evaluation problem in order to test the methodology on a real data set provided by an Italian bank.

Suggested Citation

  • Marco Corazza & Stefania Funari & Riccardo Gusso, 2012. "An evolutionary approach to preference disaggregation in a MURAME-based credit scoring problem," Working Papers 5, Venice School of Management - Department of Management, Università Ca' Foscari Venezia.
    • Handle: RePEc:vnm:wpdman:18
    as

    Download full text from publisher

    File URL: http://virgo.unive.it/wpideas/storage/2012wp5.pdf
    File Function: First version, 2012
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Doumpos, Michael & Zopounidis, Constantin, 2011. "Preference disaggregation and statistical learning for multicriteria decision support: A review," European Journal of Operational Research, Elsevier, vol. 209(3), pages 203-214, March.
    2. Nikos S. Thomaidis & Timotheos Angelidis & Vassilios Vassiliadis & Georgios Dounias, 2009. "Active Portfolio Management With Cardinality Constraints: An Application Of Particle Swarm Optimization," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 5(03), pages 535-555.
    3. Jacquet-Lagreze, Eric & Siskos, Yannis, 2001. "Preference disaggregation: 20 years of MCDA experience," European Journal of Operational Research, Elsevier, vol. 130(2), pages 233-245, April.
    4. Marco Corazza & Florence Legros & Cira Perna & Marilena Sibillo, 2017. "Mathematical and Statistical Methods for Actuarial Sciences and Finance," Post-Print hal-01776135, HAL.
    5. Doumpos, M. & Marinakis, Y. & Marinaki, M. & Zopounidis, C., 2009. "An evolutionary approach to construction of outranking models for multicriteria classification: The case of the ELECTRE TRI method," European Journal of Operational Research, Elsevier, vol. 199(2), pages 496-505, December.
    6. Willard I. Zangwill, 1967. "Non-Linear Programming Via Penalty Functions," Management Science, INFORMS, vol. 13(5), pages 344-358, January.
    7. Marco Corazza & Stefania Funari & Federico Siviero, 2008. "An MCDA-based Approach for Creditworthiness Assessment," Working Papers 177, Department of Applied Mathematics, Università Ca' Foscari Venezia.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Corazza, Marco & Funari, Stefania & Gusso, Riccardo, 2016. "Creditworthiness evaluation of Italian SMEs at the beginning of the 2007–2008 crisis: An MCDA approach," The North American Journal of Economics and Finance, Elsevier, vol. 38(C), pages 1-26.
    2. Marco Corazza & Giovanni Fasano & Stefania Funari & Riccardo Gusso, 2021. "MURAME parameter setting for creditworthiness evaluation: data-driven optimization," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(1), pages 295-339, June.
    3. Marco Corazza & Giovanni Fasano & Stefania Funari & Riccardo Gusso, 2017. "PSO-based tuning of MURAME parameters for creditworthiness evaluation of Italian SMEs," Working Papers 04, Venice School of Management - Department of Management, Università Ca' Foscari Venezia.

    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. Govindan, Kannan & Jepsen, Martin Brandt, 2016. "ELECTRE: A comprehensive literature review on methodologies and applications," European Journal of Operational Research, Elsevier, vol. 250(1), pages 1-29.
    2. Khaled Belahcène & Vincent Mousseau & Wassila Ouerdane & Marc Pirlot & Olivier Sobrie, 2023. "Multiple criteria sorting models and methods—Part I: survey of the literature," 4OR, Springer, vol. 21(1), pages 1-46, March.
    3. Khaled Belahcène & Vincent Mousseau & Wassila Ouerdane & Marc Pirlot & Olivier Sobrie, 2024. "A guided tour of multiple criteria sorting models and methods," Annals of Operations Research, Springer, vol. 343(2), pages 785-845, December.
    4. Tsionas, Mike G., 2019. "Multi-objective optimization using statistical models," European Journal of Operational Research, Elsevier, vol. 276(1), pages 364-378.
    5. Guo, Mengzhuo & Zhang, Qingpeng & Liao, Xiuwu & Chen, Frank Youhua & Zeng, Daniel Dajun, 2021. "A hybrid machine learning framework for analyzing human decision-making through learning preferences," Omega, Elsevier, vol. 101(C).
    6. Doumpos, Michael & Zopounidis, Constantin, 2011. "Preference disaggregation and statistical learning for multicriteria decision support: A review," European Journal of Operational Research, Elsevier, vol. 209(3), pages 203-214, March.
    7. Li, Zhuolin & Zhang, Zhen & Pedrycz, Witold, 2025. "Integrating machine learning models to learn potentially non-monotonic preferences for multi-criteria sorting from large-scale assignment examples," Omega, Elsevier, vol. 131(C).
    8. Liu, Jiapeng & Liao, Xiuwu & Kadziński, Miłosz & Słowiński, Roman, 2019. "Preference disaggregation within the regularization framework for sorting problems with multiple potentially non-monotonic criteria," European Journal of Operational Research, Elsevier, vol. 276(3), pages 1071-1089.
    9. Marco Corazza & Giovanni Fasano & Riccardo Gusso, 2011. "Particle Swarm Optimization with non-smooth penalty reformulation for a complex portfolio selection problem," Working Papers 2011_10, Department of Economics, University of Venice "Ca' Foscari".
    10. Cinelli, Marco & Kadziński, Miłosz & Miebs, Grzegorz & Gonzalez, Michael & Słowiński, Roman, 2022. "Recommending multiple criteria decision analysis methods with a new taxonomy-based decision support system," European Journal of Operational Research, Elsevier, vol. 302(2), pages 633-651.
    11. Ciomek, Krzysztof & Kadziński, Miłosz & Tervonen, Tommi, 2017. "Heuristics for selecting pair-wise elicitation questions in multiple criteria choice problems," European Journal of Operational Research, Elsevier, vol. 262(2), pages 693-707.
    12. Brito, Anderson J. & de Almeida, Adiel T., 2012. "Modeling a multi-attribute utility newsvendor with partial backlogging," European Journal of Operational Research, Elsevier, vol. 220(3), pages 820-830.
    13. Kadziński, Miłosz & Cinelli, Marco & Ciomek, Krzysztof & Coles, Stuart R. & Nadagouda, Mallikarjuna N. & Varma, Rajender S. & Kirwan, Kerry, 2018. "Co-constructive development of a green chemistry-based model for the assessment of nanoparticles synthesis," European Journal of Operational Research, Elsevier, vol. 264(2), pages 472-490.
    14. Martyn, Krzysztof & Kadziński, Miłosz, 2023. "Deep preference learning for multiple criteria decision analysis," European Journal of Operational Research, Elsevier, vol. 305(2), pages 781-805.
    15. Andriosopoulos, Dimitrios & Gaganis, Chrysovalantis & Pasiouras, Fotios & Zopounidis, Constantin, 2012. "An application of multicriteria decision aid models in the prediction of open market share repurchases," Omega, Elsevier, vol. 40(6), pages 882-890.
    16. Zheng, Jun & Lienert, Judit, 2018. "Stakeholder interviews with two MAVT preference elicitation philosophies in a Swiss water infrastructure decision: Aggregation using SWING-weighting and disaggregation using UTAGMS," European Journal of Operational Research, Elsevier, vol. 267(1), pages 273-287.
    17. Caroline Mota & Adiel Almeida, 2012. "A multicriteria decision model for assigning priority classes to activities in project management," Annals of Operations Research, Springer, vol. 199(1), pages 361-372, October.
    18. Fancello, Giovanna & Tsoukiàs, Alexis, 2021. "Learning urban capabilities from behaviours. A focus on visitors values for urban planning," Socio-Economic Planning Sciences, Elsevier, vol. 76(C).
    19. Catalina Bolance & Montserrat Guillen & David Pitt, 2014. "Non-parametric Models for Univariate Claim Severity Distributions - an approach using R," Working Papers 2014-01, Universitat de Barcelona, UB Riskcenter.
    20. Ellen H. Fukuda & L. M. Graña Drummond & Fernanda M. P. Raupp, 2016. "An external penalty-type method for multicriteria," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 24(2), pages 493-513, July.

    More about this item

    Keywords

    Preference disaggregation; Murame; Particle swarm optimization;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • G2 - Financial Economics - - Financial Institutions and Services

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:vnm:wpdman:18. 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: Daria Arkhipova (email available below). General contact details of provider: https://edirc.repec.org/data/mdvenit.html .

    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.