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

Li, Hui; Andina de la Fuente, Diego y 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.

Descripción

Título: Multiple proportion case-basing driven CBRE and its application in the evaluation of possible failure of firms
Autor/es:
  • Li, Hui
  • Andina de la Fuente, Diego
  • Sun, Jie
Tipo de Documento: Artículo
Título de Revista/Publicación: International Journal of Systems Science
Fecha: Agosto 2013
Volumen: 4
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Señales, Sistemas y Radiocomunicaciones
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 29483
Identificador DC: http://oa.upm.es/29483/
Identificador OAI: oai:oa.upm.es:29483
Identificador DOI: 10.1080/00207721.2012.659686
URL Oficial: http://www.tandfonline.com/doi/abs/10.1080/00207721.2012.659686
Depositado por: Memoria Investigacion
Depositado el: 28 Jul 2014 17:55
Ultima Modificación: 21 Abr 2016 23:54
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