Proceedings of the third international conference on Genetic algorithms
Neural networks for pattern recognition
Neural networks for pattern recognition
Self-Organizing Maps
Evolutionary Neural Networks for Nonlinear Dynamics Modeling
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Optimization of a Competitive Learning Neural Network by Genetic Algorithms
IWANN '93 Proceedings of the International Workshop on Artificial Neural Networks: New Trends in Neural Computation
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Genetic programming for the prediction of insolvency in non-life insurance companies
Computers and Operations Research
The class imbalance problem: A systematic study
Intelligent Data Analysis
Strongly typed genetic programming
Evolutionary Computation
A Genetic Programming Approach for Bankruptcy Prediction Using a Highly Unbalanced Database
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Predicting financial distress: a case study using self-organizing maps
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Multiobjective optimization of ensembles of multilayer perceptrons for pattern classification
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Neuroevolution with analog genetic encoding
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
Bankruptcy analysis with self-organizing maps in learning metrics
IEEE Transactions on Neural Networks
Statistical analysis of the parameters of a neuro-genetic algorithm
IEEE Transactions on Neural Networks
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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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.