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A TOPSIS Data Mining Demonstration and Application to Credit Scoring

Author

Listed:
  • Desheng Wu

    (University of Toronto, Canada)

  • David L. Olson

    (University of Nebraska, USA)

Abstract

The technique for order preference by similarity to ideal solution (TOPSIS) is a technique that can consider any number of measures, seeking to identify solutions close to an ideal and far from a nadir solution. TOPSIS has traditionally been applied in multiple criteria decision analysis. In this paper we propose an approach to develop a TOPSIS classifier. We demonstrate its use in credit scoring, providing a way to deal with large sets of data using machine learning. Data sets often contain many potential explanatory variables, some preferably minimized, some preferably maximized. Results are favorable by a comparison with traditional data mining techniques of decision trees. Proposed models are validated using Mont Carlo simulation.

Suggested Citation

  • Desheng Wu & David L. Olson, 2006. "A TOPSIS Data Mining Demonstration and Application to Credit Scoring," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 2(3), pages 16-26, July.
    • Handle: RePEc:igg:jdwm00:v:2:y:2006:i:3:p:16-26
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    Cited by:

    1. Ouenniche, Jamal & Pérez-Gladish, Blanca & Bouslah, Kais, 2018. "An out-of-sample framework for TOPSIS-based classifiers with application in bankruptcy prediction," Technological Forecasting and Social Change, Elsevier, vol. 131(C), pages 111-116.
    2. Başak Bulut Karageyik & Şule Şahin, 2017. "Determination of the Optimal Retention Level Based on Different Measures," JRFM, MDPI, vol. 10(1), pages 1-21, January.

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