Towards Objective Data Selection in Bankruptcy Prediction

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  title =        "Towards Objective Data Selection in Bankruptcy
  author =       "Sverre Gunnersen and Kate Smith-Miles and 
                 Vincent Lee",
  pages =        "9--16",
  booktitle =    "Proceedings of the 2012 IEEE Congress on Evolutionary
  year =         "2012",
  editor =       "Xiaodong Li",
  month =        "10-15 " # jun,
  DOI =          "doi:10.1109/CEC.2012.6256129",
  address =      "Brisbane, Australia",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Conflict of
                 Interest Papers, Computational Intelligence in Finance,
                 Economics and Management Sciences (IEEE-CEC),
                 Large-scale problems.",
  abstract =     "This paper proposes and tests a methodology for
                 selecting features and test cases with the goal of
                 improving medium term bankruptcy prediction accuracy in
                 large uncontrolled datasets of financial records. We
                 propose a Genetic Programming and Neural Network based
                 objective feature selection methodology to identify key
                 inputs, and then use those inputs to combine
                 multi-level Self-Organising Maps with Spectral
                 Clustering to build clusters. Performing objective
                 feature selection within each of those clusters, this
                 research was able to increase out-of-sample
                 classification accuracy from 71.3percent and
                 69.8percent on the Genetic Programming and Neural
                 Network models respectively to 80.0percent and
  notes =        "WCCI 2012. CEC 2012 - A joint meeting of the IEEE, the
                 EPS and the IET.",

Genetic Programming entries for Sverre Gunnersen Kate Smith-Miles Vincent Lee