Multiclass Corporate Failure Prediction by Adaboost.M1

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Predicting corporate failure is an important management science problem. This is a typical classification question where the objective is to determine which indicators are involved in the failure or success of a corporation. Despite the complexity of the matter, a two-class problem has usually been considered to tackle this classification task. The objective of this paper is twofold. On the one hand, we apply the Adaboost.M1 algorithm to improve the accuracy of a classification tree in a multiclass corporate failure prediction problem using a set of European firms. On the other, we introduce novel discerning measures to rank independent variables in a generic classification task.

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Fig. 1


  1. 1.

    In this paper, only the information from the previous year was used, but this is a part of a larger research. That is the reason for the requirement of complete data available for the five previous years

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    The R program is a set of packages for data manipulation, calculus, and graphics (R Development Core Team 2004). Among other characteristics, it has a well-developed and effective-programming language (R language). The R program has much in common with the well known S-Plus program, but unlike the latter, R is a distribution-free program available on the web at


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Correspondence to Esteban Alfaro Cortés.



Table A1 Predictor Variables

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Alfaro Cortés, E., Gámez Martínez, M. & García Rubio, N. Multiclass Corporate Failure Prediction by Adaboost.M1. Int Adv Econ Res 13, 301–312 (2007).

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  • Corporate failure prediction
  • Ensemble classifiers
  • Adaboost.M1


  • C10
  • G30
  • M00