Comparing extended classifier system and genetic programming for financial forecasting: an empirical study

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

@Article{journals/soco/ChenCCHH07,
  author =       "Mu-Yen Chen and Kuang-Ku Chen and Heien-Kun Chiang and 
                 Hwa-Shan Huang and Mu-Jung Huang",
  title =        "Comparing extended classifier system and genetic
                 programming for financial forecasting: an empirical
                 study",
  journal =      "Soft Computing",
  year =         "2007",
  volume =       "11",
  number =       "12",
  pages =        "1173--1183",
  keywords =     "genetic algorithms, genetic programming, Learning
                 classifier system, Extended classifier system, Machine
                 learning",
  DOI =          "doi:10.1007/s00500-007-0161-3",
  abstract =     "As a broad subfield of artificial intelligence,
                 machine learning is concerned with the development of
                 algorithms and techniques that allow computers to
                 learn. These methods such as fuzzy logic, neural
                 networks, support vector machines, decision trees and
                 Bayesian learning have been applied to learn meaningful
                 rules; however, the only drawback of these methods is
                 that it often gets trapped into a local optimal. In
                 contrast with machine learning methods, a genetic
                 algorithm (GA) is guaranteeing for acquiring better
                 results based on its natural evolution and global
                 searching. GA has given rise to two new fields of
                 research where global optimization is of crucial
                 importance: genetic based machine learning (GBML) and
                 genetic programming (GP). This article adopts the GBML
                 technique to provide a three-phase knowledge extraction
                 methodology, which makes continues and instant learning
                 while integrates multiple rule sets into a centralized
                 knowledge base. Moreover, the proposed system and GP
                 are both applied to the theoretical and empirical
                 experiments. Results for both approaches are presented
                 and compared. This paper makes two important
                 contributions: (1) it uses three criteria (accuracy,
                 coverage, and fitness) to apply the knowledge
                 extraction process which is very effective in selecting
                 an optimal set of rules from a large population; (2)
                 the experiments prove that the rule sets derived by the
                 proposed approach are more accurate than GP.",
  bibdate =      "2008-03-11",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/journals/soco/soco11.html#ChenCCHH07",
}

Genetic Programming entries for Mu-Yen Chen Kuang-Ku Chen Heien-Kun Chiang Hwa-Shan Huang Mu-Jung Huang

Citations