Comparing Multiobjective Evolutionary Ensembles for Minimizing Type I and II Errors for Bankruptcy Prediction

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Alfaro-Cid:2008:cec,

• author = "E. Alfaro-Cid and P. A. Castillo and A. Esparcia and K. Sharman and J. J. Merelo and A. Prieto and J. L. J. Laredo",
• title = "Comparing Multiobjective Evolutionary Ensembles for Minimizing Type I and II Errors for Bankruptcy Prediction",
• booktitle = "2008 IEEE World Congress on Computational Intelligence",
• year = "2008",
• editor = "Jun Wang",
• pages = "2902--2908",
• address = "Hong Kong",
• month = "1-6 " # jun,
• organization = "IEEE Computational Intelligence Society",
• publisher = "IEEE Press",
• isbn13 = "978-1-4244-1823-7",
• file = "EC0649.pdf",
• DOI = "doi:10.1109/CEC.2008.4631188",
• abstract = "In many real world applications type I (false positive) and type II (false negative) errors have to be dealt with separately, which is a complex problem since an attempt to minimise one of them usually makes the other grow. In fact, a type of error can be more important than the other, and a trade-off that minimises the most important error type must be reached. In the case of the bankruptcy prediction problem the error type II is of greater importance, being unable to identify that a company is at risk causes problems to creditors and slows down the taking of measures that may solve the problem. Despite the importance of type II errors, most bankruptcy prediction methods take into account only the global classification error. In this paper we propose and compare two methods to optimise both error types in classification: artificial neural networks and function trees ensembles created through multiobjective Optimization. Since the multiobjective Optimization process produces a set of equally optimal results (Pareto front) the classification of the test patterns in both cases is based on the non-dominated solutions acting as an ensemble. The experiments prove that, although the best classification rates are obtained using the artificial neural network, the multiobjective genetic programming model is able to generate comparable results in the form of an analytical function.",
• keywords = "genetic algorithms, genetic programming",
• notes = "WCCI 2008 - A joint meeting of the IEEE, the INNS, the EPS and the IET.",

}

Genetic Programming entries for Eva Alfaro-Cid Pedro A Castillo Valdivieso Anna Esparcia-Alcazar Kenneth C Sharman Juan Julian Merelo Alberto Prieto Espinosa Juan L J Laredo

Citations