A TOPSIS Data Mining Demonstration and Application to Credit Scoring

Desheng Wu and David L. Olson
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Desheng Wu: University of Toronto, Canada
David L. Olson: University of Nebraska, USA

International Journal of Data Warehousing and Mining (IJDWM), 2006, vol. 2, issue 3, 16-26

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.

Date: 2006
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