Using GP to Evolve Decision Rules for Classification in Financial Data Sets

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

@InProceedings{wang:2010:ICCI,
  author =       "Pu Wang and Edward P. K. Tsang and Thomas Weise and 
                 Ke Tang and Xin Yao",
  title =        "Using GP to Evolve Decision Rules for Classification
                 in Financial Data Sets",
  booktitle =    "9th IEEE International Conference on Cognitive
                 Informatics (ICCI 2010)",
  year =         "2010",
  editor =       "Fuchun Sun and Yingxu Wang and Jianhua Lu and 
                 Bo Zhang and Witold Kinsner and Lotfi A. Zadeh",
  pages =        "722--727",
  address =      "Tsinghua University, Beijing, China",
  month =        "7-9 " # jul,
  publisher =    "IEEE",
  note =         "Special Session on Evolutionary Computing",
  email =        "tweise@gmx.de",
  keywords =     "genetic algorithms, genetic programming, data mining,
                 financial data sets, genetic decision trees, Decision
                 rules, Classification, Forecasting, Finance, EDDIE,
                 FGP, AUC, Entropy, financial forecasting, genetic
                 programming approach, investment, machine learning,
                 financial data processing, investment, learning
                 (artificial intelligence), pattern classification",
  isbn13 =       "978-1-4244-8040-1",
  URL =          "http://www.it-weise.de/documents/files/WTWTY2010UGPTEDRFCIFDS.pdf",
  URL =          "http://home.ustc.edu.cn/~wuyou308/publications/paper1.pdf",
  DOI =          "doi:10.1109/COGINF.2010.5599820",
  size =         "9 pages",
  abstract =     "Financial forecasting is a lucrative and complicated
                 application of machine learning. In this paper, we
                 focus on the finding investment opportunities. We
                 therefore explore four different Genetic Programming
                 approaches and compare their performances on real-world
                 data. We find that the novelties we introduced in some
                 of these approaches indeed improve the results.
                 However, we also show that the Genetic Programming
                 process itself is still very inefficient and that
                 further improvements are necessary if we want this
                 application of GP to become successful.",
  notes =        "http://www.icci2010.edu.cn/ Also known as
                 \cite{5599820}",
}

Genetic Programming entries for Pu Wang Edward P K Tsang Thomas Weise Ke Tang Xin Yao

Citations