Projecting Financial Data using Genetic Programming in Classification and Regression Tasks

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{eurogp06:EstebanezVallsAler,
  author =       "C\'esar Est\'ebanez and Jos\'e M. Valls and 
                 Ricardo Aler",
  title =        "Projecting Financial Data using Genetic Programming in
                 Classification and Regression Tasks",
  editor =       "Pierre Collet and Marco Tomassini and Marc Ebner and 
                 Steven Gustafson and Anik\'o Ek\'art",
  booktitle =    "Proceedings of the 9th European Conference on Genetic
                 Programming",
  publisher =    "Springer",
  series =       "Lecture Notes in Computer Science",
  volume =       "3905",
  year =         "2006",
  address =      "Budapest, Hungary",
  month =        "10 - 12 " # apr,
  organisation = "EvoNet",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-33143-3",
  pages =        "202--212",
  DOI =          "doi:10.1007/11729976_18",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  abstract =     "The use of Constructive Induction (CI) methods for the
                 generation of high-quality attributes is a very
                 important issue in Machine Learning. In this paper, we
                 present a CI method based in Genetic Programming (GP).
                 This method is able to evolve projections that
                 transform the dataset, constructing a new coordinates
                 space in which the data can be more easily predicted.
                 This coordinates space can be smaller than the original
                 one, achieving two main goals at the same time: on one
                 hand, improving classification tasks; on the other
                 hand, reducing dimensionality of the problem. Also, our
                 method can handle classification and regression
                 problems. We have tested our approach in two financial
                 prediction problems because their high dimensionality
                 is very appropriate for our method. In the first one,
                 GP is used to tackle prediction of bankruptcy of
                 companies (classification problem). In the second one,
                 an IPO Underpricing prediction domain (a classical
                 regression problem) is confronted. Our method obtained
                 in both cases competitive results and, in addition, it
                 drastically reduced dimensionality of the problem.",
  notes =        "Part of \cite{collet:2006:GP} EuroGP'2006 held in
                 conjunction with EvoCOP2006 and EvoWorkshops2006",
}

Genetic Programming entries for Cesar Estebanez Jose Maria Valls Ferran Ricardo Aler Mur

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