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A TOPSIS Data Mining Demonstration and Application to Credit Scoring

A TOPSIS Data Mining Demonstration and Application to Credit Scoring

Desheng Wu, David L. Olson
Copyright: © 2006 |Volume: 2 |Issue: 3 |Pages: 11
ISSN: 1548-3924|EISSN: 1548-3932|ISSN: 1548-3924|EISBN13: 9781615202119|EISSN: 1548-3924|DOI: 10.4018/jdwm.2006070102
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MLA

Wu, Desheng, and David L. Olson. "A TOPSIS Data Mining Demonstration and Application to Credit Scoring." IJDWM vol.2, no.3 2006: pp.16-26. http://doi.org/10.4018/jdwm.2006070102

APA

Wu, D. & Olson, D. L. (2006). A TOPSIS Data Mining Demonstration and Application to Credit Scoring. International Journal of Data Warehousing and Mining (IJDWM), 2(3), 16-26. http://doi.org/10.4018/jdwm.2006070102

Chicago

Wu, Desheng, and David L. Olson. "A TOPSIS Data Mining Demonstration and Application to Credit Scoring," International Journal of Data Warehousing and Mining (IJDWM) 2, no.3: 16-26. http://doi.org/10.4018/jdwm.2006070102

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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.

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