A TOPSIS Data Mining Demonstration and Application to Credit Scoring

View Sample PDF

Author(s): Desheng Wu (University of Toronto, Canada)and David L. Olson (University of Nebraska, USA)
Copyright: 2008
Pages: 11
Source title: Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-59904-951-9.ch112

Keywords: Data Mining and Databases / Data Warehousing / Information Science Reference / Library & Information Science

Purchase

View A TOPSIS Data Mining Demonstration and Application to Credit Scoring on the publisher's website for pricing and purchasing information.

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.

The IRMA Community

Research IRM

A TOPSIS Data Mining Demonstration and Application to Credit Scoring

Purchase

Abstract

Related Content

IRMA Sponsors