Evolving Decision Rules to Predict Investment Opportunities

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Alma Lilia Garcia-Almanza and Edward P. K. Tsang",
  title =        "Evolving Decision Rules to Predict Investment
  journal =      "International Journal of Automation and Computing",
  year =         "2008",
  volume =       "5",
  number =       "1",
  pages =        "22--31",
  month =        jan,
  keywords =     "genetic algorithms, genetic programming, Machine
                 learning, classification, imbalanced classes, evolution
                 of rules",
  publisher =    "Institute of Automation, Chinese Academy of Sciences,
                 co-published with Springer-Verlag GmbH",
  ISSN =         "1476-8186",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=",
  DOI =          "doi:10.1007/s11633-008-0022-2",
  size =         "10 pages",
  abstract =     "This paper is motivated by the interest in finding
                 significant movements in financial stock prices.
                 However, when the number of profitable opportunities is
                 scarce, the prediction of these cases is difficult. In
                 a previous work, we have introduced evolving decision
                 rules (EDR) to detect financial opportunities. The
                 objective of EDR is to classify the minority class
                 (positive cases) in imbalanced environments. EDR
                 provides a range of classifications to find the best
                 balance between not making mistakes and not missing
                 opportunities. The goals of this paper are: 1) to show
                 that EDR produces a range of solutions to suit the
                 investor's preferences and 2) to analyse the factors
                 that benefit the performance of EDR. A series of
                 experiments was performed. EDR was tested using a data
                 set from the London Financial Market. To analyze the
                 EDR behaviour, another experiment was carried out using
                 three artificial data sets, whose solutions have
                 different levels of complexity. Finally, an
                 illustrative example was provided to show how a bigger
                 collection of rules is able to classify more positive
                 cases in imbalanced data sets. Experimental results
                 show that: 1) EDR offers a range of solutions to fit
                 the risk guidelines of different types of investors,
                 and 2) a bigger collection of rules is able to classify
                 more positive cases in imbalanced environments.",
  affiliation =  "University of Essex Department of Computer Science
                 Wivenhoe Park Colchester CO4 3SQ UK",

Genetic Programming entries for Alma Lilia Garcia Almanza Edward P K Tsang