Multiclass Corporate Failure Prediction by Adaboost.M1 |
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| Authors: | Esteban Alfaro Cortés Matías Gámez Martínez Noelia García Rubio |
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| Institution: | (1) Economic and Business Sciences Faculty of Albacete, Castilla-La Mancha University, Plaza de la Universidad 1, 02071 Albacete, Spain |
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| Abstract: | 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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| Keywords: | Corporate failure prediction Ensemble classifiers Adaboost M1 |
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