Applying support vector machines and mutual information to book losses prediction

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

  author =       "A. M. Mora and L. J. Herrera and J. Urquiza and 
                 I. Rojas and J. J. Merelo",
  title =        "Applying support vector machines and mutual
                 information to book losses prediction",
  booktitle =    "International Joint Conference on Neural Networks
                 (IJCNN 2010)",
  year =         "2010",
  address =      "Barcelona, Spain",
  month =        "18-23 " # jul,
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, SVM",
  isbn13 =       "978-1-4244-6917-8",
  abstract =     "This work presents a feasible solution to the problem
                 of book losses prediction from financial and general
                 data in companies. The specific problem tackled in this
                 work corresponds to a real dataset of Spanish
                 companies. A Mutual Information-based criterion has
                 been applied in order to reduce the initial set of
                 variables, and a Support Vector Machine classifier has
                 been designed to perform the prediction. The results
                 show that the proposed approach obtains an important
                 reduction of the number of variables needed to perform
                 the prediction, improving the generalisation
                 capabilities of the model. The accuracy rates were
                 above the 84percent in the test set, much better than
                 those obtained by other soft-computing algorithms (such
                 as Genetic Programming, Self-Organising Maps or
                 Artificial Neural Networks) working with the same
                 dataset and presented in previous works. The proposed
                 approach shows to be promising and could be determinant
                 in providing the experts with the right tools for the
                 selection of the relevant factors and for the
                 prediction in this difficult problem.",
  DOI =          "doi:10.1109/IJCNN.2010.5596710",
  notes =        "WCCI 2010. Also known as \cite{5596710}",

Genetic Programming entries for Antonio M Mora Garcia Luis Javier Herrera Maldonado Jose Miguel Urquiza Ortiz Ignacio Rojas Ruiz Juan Julian Merelo