Journal
COMPUTERS & OPERATIONS RESEARCH
Volume 38, Issue 2, Pages 409-419Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cor.2010.06.008
Keywords
TOPSIS; Multiple criteria case-based reasoning; Business failure prediction; Similarities to positive and negative ideal cases
Categories
Funding
- National Natural Science Foundation of China [70801055]
- Zhejiang Provincial Natural Science Foundation of China [Y6090392]
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Case-based reasoning (CBR) solves many real-world problems under the assumption that similar observations have similar outputs. As an implementation of this assumption and inspired by the technique for order performance by the similarity to ideal solution (TOPSIS), this paper proposes a new type of multiple criteria CBR method for binary business failure prediction (BFP) with similarities to positive and negative ideal cases (SPNIC). Assuming that the binary prediction of business failure generates two results, i.e., failure and non-failure, we set the principle of this CBR forecasting method which is termed as SPNIC-based CBR as follows: new observations should have the same output as the positive or negative ideal case to which they are more similar. From the perspective of CBR, the SPNIC-based CBR forecasting method consists of R(4) processes: retrieving positive and negative ideal cases, reusing solutions of ideal cases to forecast, retain cases, and reconstruct the case base. As a demonstration, we applied this method to forecast business failure in China with three data representations of a formerly collected dataset from normal economic environment and a representation of a recently collected dataset from financial crisis environment. The results indicate that this new CBR forecasting method can produce significantly better short-term discriminate capability than comparative methods, except for support vector machine, in normal economic environment: On the contrary, it cannot produce acceptable performance in financial crisis environment. Further topics about this method are discussed. (C) 2010 Elsevier Ltd. All rights reserved,
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