Comparing Multiobjective Evolutionary Ensembles for Minimizing Type I and II Errors for Bankruptcy Prediction
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Alfaro-Cid:2008:cec,
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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",
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title = "Comparing Multiobjective Evolutionary Ensembles for
Minimizing Type I and II Errors for Bankruptcy
Prediction",
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booktitle = "2008 IEEE World Congress on Computational
Intelligence",
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year = "2008",
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editor = "Jun Wang",
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pages = "2902--2908",
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address = "Hong Kong",
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month = "1-6 " # jun,
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organization = "IEEE Computational Intelligence Society",
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publisher = "IEEE Press",
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isbn13 = "978-1-4244-1823-7",
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file = "EC0649.pdf",
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DOI = "doi:10.1109/CEC.2008.4631188",
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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.",
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keywords = "genetic algorithms, genetic programming",
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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