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A new EDAS-based in-sample-out-of-sample classifier for risk-class prediction

Jamal Ouenniche (Department of Management Science and Business Economics, University of Edinburgh Business School, Edinburgh, UK)
Oscar Javier Uvalle Perez (Department of Management Science and Business Economics, University of Edinburgh Business School, Edinburgh, UK)
Aziz Ettouhami (Conception and Systems Laboratory, Universite Mohammed V de Rabat, Rabat, Morocco)

Management Decision

ISSN: 0025-1747

Article publication date: 7 August 2018

Issue publication date: 26 February 2019

316

Abstract

Purpose

Nowadays, the field of data analytics is witnessing an unprecedented interest from a variety of stakeholders. The purpose of this paper is to contribute to the subfield of predictive analytics by proposing a new non-parametric classifier.

Design/methodology/approach

The proposed new non-parametric classifier performs both in-sample and out-of-sample predictions, where in-sample predictions are devised with a new Evaluation Based on Distance from Average Solution (EDAS)-based classifier, and out-of-sample predictions are devised with a CBR-based classifier trained on the class predictions provided by the proposed EDAS-based classifier.

Findings

The performance of the proposed new non-parametric classification framework is tested on a data set of UK firms in predicting bankruptcy. Numerical results demonstrate an outstanding predictive performance, which is robust to the implementation decisions’ choices.

Practical implications

The exceptional predictive performance of the proposed new non-parametric classifier makes it a real contender in actual applications in areas such as finance and investment, internet security, fraud and medical diagnosis, where the accuracy of the risk-class predictions has serious consequences for the relevant stakeholders.

Originality/value

Over and above the design elements of the new integrated in-sample-out-of-sample classification framework and its non-parametric nature, it delivers an outstanding predictive performance for a bankruptcy prediction application.

Keywords

Citation

Ouenniche, J., Uvalle Perez, O.J. and Ettouhami, A. (2019), "A new EDAS-based in-sample-out-of-sample classifier for risk-class prediction", Management Decision, Vol. 57 No. 2, pp. 314-323. https://doi.org/10.1108/MD-04-2018-0397

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

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