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June 9, 2019
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Multiple proportion case-basing driven CBRE and its application in the evaluation of possible failure of firms

Publicated to: INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE. 44 (8): 1409-1425 - 2013-08-01 44(8), DOI: 10.1080/00207721.2012.659686

Authors:

Li, H; Andina, D; Sun, J
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Affiliations

Tech Univ Madrid, Grp Automat Signal & Commun, ETSI Telecomunicac, Madrid 28040, Spain - Author
Zhejiang Normal Univ, Sch Econ & Management, Jinhua 321004, Zhejiang, Peoples R China - Author

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 CBR's predictive ability, outperformed all the comparative methods.
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Keywords

Bankruptcy predictionBusiness failureCase-based reasoning ensembleDefect classificationEvaluation of possible failure of firmsFault-detectionFinancial distress predictionGenetic algorithmMultiple case-basingNearest neighbour ensembleNearest-neighbor classifiersProportion baggingReasoning approachSpecial-issueSystem

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2013, it was in position 27/59, thus managing to position itself as a Q1 (Primer Cuartil), in the category Automation & Control Systems.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-24:

  • Google Scholar: 11
  • WoS: 7
  • Scopus: 9
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-24:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 13 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/29483/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 429
  • Downloads: 344
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: China.

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Awards linked to the item

This research is partially supported by the National Natural Science Foundation of China (no. 71171179), the Zhejiang Provincial Philosophy and Social Science Foundation Zhijiang Young Talent of Social Science (11ZJQN081YB), and the Zhejiang Provincial Natural Science Foundation of China (no. Y7100008). The authors gratefully thank anonymous referees for their useful comments and editors for their work.
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