Evolutionary Applications for Financial Prediction: Classification Methods to Gather Patterns Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Book{Garcia-Almanza:book,
  author =       "Alma Lilia {Garcia Almanza} and Edward Tsang",
  title =        "Evolutionary Applications for Financial Prediction:
                 Classification Methods to Gather Patterns Using Genetic
                 Programming",
  publisher =    "VDM Verlag Dr. Muller",
  year =         "2011",
  address =      "Saarbrucken, Germany",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-639-30767-4",
  URL =          "http://www.bracil.net/finance/GarciaTsang-book2011/",
  URL =          "http://www.amazon.com/Evolutionary-Applications-Financial-Prediction-Classification/dp/3639307674/ref=sr_1_1?ie=UTF8&qid=1305383401&sr=8-1",
  abstract =     "This book presents three applications, based on
                 Machine Learning and Genetic Programming, which are
                 devoted to find useful patterns to predict future
                 events. The objective is to train the algorithms by
                 using past data to produce a classifier that identifies
                 the positive cases and discriminates the false alarms.
                 This work uses examples for predicting future
                 opportunities in financial stock markets in cases where
                 the number of profitable opportunities is scarce.
                 However, when the number of positive examples is small
                 in comparison with the number of total cases, the
                 identification of useful patterns becomes a serious
                 challenge. Nevertheless, the objective of many real
                 world problems, is precisely to identify the minority
                 class as the fraud detection problem, or medical
                 diagnosis and many other examples. The techniques of
                 this book are suitable to deal with imbalanced data
                 sets, provide comprehensible results that allow users
                 to understand the factors that are involved in the
                 decision, as well as to generate a range of solutions
                 that let the user choose the best trade off according
                 to their risk preferences.",
  notes =        "Reviewed by \cite{LeBaron:2012:GPEM}",
  size =         "172 pages",
}

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

Citations