On the utility of input selection and pruning for financial distress prediction models
Becerra, V. M., Galvão, R. K. H. and Abou-Seada, M. (2002) On the utility of input selection and pruning for financial distress prediction models. In: International Joint Conference on Neural Networks: IJCNN 2002, 12-17 May 2002, Honolulu, HI, USA, pp. 1328-1333.
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To link to this article DOI: 10.1109/IJCNN.2002.1007687
Analyzes the use of linear and neural network models for financial distress classification, with emphasis on the issues of input variable selection and model pruning. A data-driven method for selecting input variables (financial ratios, in this case) is proposed. A case study involving 60 British firms in the period 1997-2000 is used for illustration. It is shown that the use of the Optimal Brain Damage pruning technique can considerably improve the generalization ability of a neural model. Moreover, the set of financial ratios obtained with the proposed selection procedure is shown to be an appropriate alternative to the ratios usually employed by practitioners.