Evolving Neural-Symbolic Systems Guided by Adaptive Training Schemes: Applications in Finance

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

@Article{journals/aai/TsakonasD07,
  author =       "Athanasios Tsakonas and Georgios Dounias",
  title =        "Evolving Neural-Symbolic Systems Guided by Adaptive
                 Training Schemes: Applications in Finance",
  journal =      "Applied Artificial Intelligence",
  volume =       "21",
  number =       "7",
  year =         "2007",
  pages =        "681--706",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, adaptive
                 training, symbolic connectionist systems, neural logic
                 networks, grammar-guided genetic",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.2917",
  DOI =          "doi:10.1080/08839510701492603",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  contributor =  "CiteSeerX",
  language =     "en",
  oai =          "oai:CiteSeerXPSU:10.1.1.149.2917",
  abstract =     "The paper presents a hybrid and adaptive intelligent
                 methodology, based on neural logic networks and
                 grammar-guided genetic programming. The aim of the
                 study is to demonstrate how to generate efficient
                 neural logic networks with the aid of genetic
                 programming methods trained adaptively through an
                 innovative scheme. The proposed adaptive training
                 scheme of the genetic programming mechanism, leads to
                 the generation of high diversity solutions and small
                 sized individuals. The overall methodology is
                 advantageous due to the adaptive training scheme
                 proposed, for offering both, accurate and interpretable
                 results in the form of expert rules. Moreover, a
                 sensitivity analysis study is provided within the
                 paper, comparing the performance of the proposed
                 evolutionary neural logic networks methodology, with
                 well-known competitive inductive machine learning
                 approaches. Two financial domains of application have
                 been selected to demonstrate the capabilities of the
                 proposed methodology, (a) classification of credit
                 applicants for consumer loans of a German bank and (b)
                 the credit-scoring decision-making process in an
                 Australian bank. Results seem encouraging since the
                 proposed methodology outperforms a number of
                 competitive existing statistical and intelligent
                 methodologies, while it also produces handy decision
                 rules, short in length and transparent in meaning and
                 use.",
}

Genetic Programming entries for Athanasios D Tsakonas Georgios Dounias

Citations