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

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.

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