Summary
This chapter illustrates the usefulness of Kohonen’s self organizing maps (SOMs) and genetic programming (GP) for identifying relevant variables in a financial distress prediction problem. The approach presented here uses GP as a classification/prediction tool to produce models that can predict if a company is going to have book losses in the future. In addition, the analysis of the resulting GP trees provides information about the relevance of certain variables when solving the prediction model. This analysis in combination with the conclusions yielded using a SOM allowed us to significantly reduce the number of variables used to solve the book losses prediction problem while improving the error rates obtained.
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Alfaro-Cid, E., Mora, A.M., Merelo, J.J., Esparcia-Alcázar, A.I., Sharman, K. (2009). Finding Relevant Variables in a Financial Distress Prediction Problem Using Genetic Programming and Self-organizing Maps. In: Brabazon, A., O’Neill, M. (eds) Natural Computing in Computational Finance. Studies in Computational Intelligence, vol 185. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-95974-8_3
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DOI: https://doi.org/10.1007/978-3-540-95974-8_3
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