In this work we compare two soft-computing methods for producing models that are able to predict whether a company is going to have book losses: artificial neural networks (ANNs) and genetic programming (GP). In order to build prediction models that can be applied to an extensive number of practical cases, we need simple models which require a small amount of data. Kohonen's self-organizing map (SOM) is a non-supervised neural network that is usually used as a clustering tool. In our case a SOM has been used to reduce the dimensions of the prediction problem. Traditionally, ANNs have been considered able to produce better classifier structures than GP. In this work we merge the capability of GP for generating classification trees and the feature extraction abilities of SOM, obtaining a classification tool that beats the results yielded using an evolutionary ANN method.
Mora, Castillo, Merelo, EsparciaAlcazar, and Sharman REF compared the effectiveness of genetic programming (GP) merged with self-organizing maps (SOM) to an evolutionary ANN method for discovering causes of financial distress.
Discovering causes of financial distress by combining evolutionary algorithms and artificial neural networks
GECCO '08
Computer Science