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

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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",
  title =        "Comparing Multiobjective Evolutionary Ensembles for
                 Minimizing Type I and II Errors for Bankruptcy
  booktitle =    "2008 IEEE World Congress on Computational
  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
  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