Multiple proportion case-basing driven CBRE and its application in the evaluation of possible failure of firms

Li, Hui and Andina de la Fuente, Diego and Sun, Jie (2013). Multiple proportion case-basing driven CBRE and its application in the evaluation of possible failure of firms. "International Journal of Systems Science", v. 4 (n. 8); pp. 1409-1425. ISSN 0020-7721. https://doi.org/10.1080/00207721.2012.659686.

Description

Title: Multiple proportion case-basing driven CBRE and its application in the evaluation of possible failure of firms
Author/s:
  • Li, Hui
  • Andina de la Fuente, Diego
  • Sun, Jie
Item Type: Article
Título de Revista/Publicación: International Journal of Systems Science
Date: August 2013
Volume: 4
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Señales, Sistemas y Radiocomunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Case-based reasoning (CBR) is a unique tool for the evaluation of possible failure of firms (EOPFOF) for its eases of interpretation and implementation. Ensemble computing, a variation of group decision in society, provides a potential means of improving predictive performance of CBR-based EOPFOF. This research aims to integrate bagging and proportion case-basing with CBR to generate a method of proportion bagging CBR for EOPFOF. Diverse multiple case bases are first produced by multiple case-basing, in which a volume parameter is introduced to control the size of each case base. Then, the classic case retrieval algorithm is implemented to generate diverse member CBR predictors. Majority voting, the most frequently used mechanism in ensemble computing, is finally used to aggregate outputs of member CBR predictors in order to produce final prediction of the CBR ensemble. In an empirical experiment, we statistically validated the results of the CBR ensemble from multiple case bases by comparing them with those of multivariate discriminant analysis, logistic regression, classic CBR, the best member CBR predictor and bagging CBR ensemble. The results from Chinese EOPFOF prior to 3 years indicate that the new CBR ensemble, which significantly improved CBRs predictive ability, outperformed all the comparative methods.

More information

Item ID: 29483
DC Identifier: http://oa.upm.es/29483/
OAI Identifier: oai:oa.upm.es:29483
DOI: 10.1080/00207721.2012.659686
Official URL: http://www.tandfonline.com/doi/abs/10.1080/00207721.2012.659686
Deposited by: Memoria Investigacion
Deposited on: 28 Jul 2014 17:55
Last Modified: 21 Apr 2016 23:54
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