The importance of simplicity and validation in genetic programming for data mining in financial data

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

@InProceedings{thomas:1999:isvGPdmfd,
  author =       "James D Thomas and Katia Sycara",
  title =        "The importance of simplicity and validation in genetic
                 programming for data mining in financial data",
  booktitle =    "Data Mining with Evolutionary Algorithms: Research
                 Directions",
  year =         "1999",
  editor =       "Alex Alves Freitas",
  pages =        "7--11",
  address =      "Orlando, Florida",
  publisher_address = "445 Burgess Drive, Menlo Park, California 94025,
                 USA",
  month =        "18 " # jul,
  publisher =    "AAAI Press",
  note =         "Technical Report WS-99-06",
  keywords =     "genetic algorithms, genetic programming, data mining",
  ISBN =         "1-57735-090-1",
  URL =          "http://www.cs.cmu.edu/afs/cs/user/jthomas/Web/Papers/gecco99.ps",
  URL =          "http://www.ri.cmu.edu/pub_files/pub2/thomas_james_1999_2/thomas_james_1999_2.pdf",
  URL =          "http://citeseer.ist.psu.edu/323257.html",
  size =         "5 pages",
  abstract =     "A genetic programming system for data mining trading
                 rules out of past foreign exchange data is described.
                 The system is tested on real data from the dollar/yen
                 and dollar/DM markets, and shown to produce
                 considerable excess returns in the dollar/yen market.
                 Design issues relating to potential rule complexity and
                 validation regimes are explored empirically. Keeping
                 potential rules as simple as possible is shown to be
                 the most important component of success. Validation
                 issues are more complicated. Inspection of fitness on a
                 validation set is used to cut-off search in hopes of
                 avoiding overfitting. Additional attempts to use the
                 validation set to improve performance are shown to be
                 ineffective in the standard framework. An examination
                 of correlations between performance on the validation
                 set and on the test set leads to an understanding of
                 how such measures can be marginally benificial;
                 unfortunately, this suggests that further attemps to
                 improve performance through validation will prove
                 difficult",
  notes =        "Joint AAAI-99 & GECCO-99 Workshop. Workshop
                 information at http://www.ppgia.pucpr.br/~dmea/",
}

Genetic Programming entries for James D Thomas Katia Sycara

Citations