An Architecture-Altering and Training Methodology for Neural Logic Networks: Application in the Banking Sector

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

@InProceedings{conf/anns/TsakonasD05,
  author =       "Athanasios Tsakonas and Georgios Dounias",
  title =        "An Architecture-Altering and Training Methodology for
                 Neural Logic Networks: Application in the Banking
                 Sector",
  editor =       "Kurosh Madani",
  booktitle =    "Proceedings of the 1st International Workshop on
                 Artificial Neural Networks and Intelligent Information
                 Processing, ANNIIP 2005,",
  year =         "2005",
  pages =        "82--93",
  address =      "Barcelona, Spain",
  month =        sep,
  publisher =    "INSTICC Press",
  note =         "In conjunction with ICINCO 2005",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "972-8865-36-8",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.8601",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  contributor =  "CiteSeerX",
  language =     "en",
  oai =          "oai:CiteSeerXPSU:10.1.1.149.8601",
  abstract =     "Neural logic networks, Grammar-guided genetic
                 programming, Credit scoring Artificial neural networks
                 have been universally acknowledged for their ability on
                 constructing forecasting and classifying systems. Among
                 their desirable features, it has always been the
                 interpretation of their structure, aiming to provide
                 further knowledge for the domain experts. A number of
                 methodologies have been developed for this reason. One
                 such paradigm is the neural logic networks concept.
                 Neural logic networks have been especially designed in
                 order to enable the interpretation of their structure
                 into a number of simple logical rules and they can be
                 seen as a network representation of a logical rule
                 base. Although powerful by their definition in this
                 context, neural logic networks have performed poorly
                 when used in approaches that required training from
                 data. Standard training methods, such as the
                 back-propagation, require the network's synapse weight
                 altering, which destroys the network's
                 interpretability. The methodology in this paper
                 overcomes these problems and proposes an
                 architecture-altering technique, which enables the
                 production of highly antagonistic solutions while
                 preserving any weight-related information. The
                 implementation involves genetic programming using a
                 grammar-guided training approach, in order to provide
                 arbitrarily large and connected neural logic networks.
                 The methodology is tested in a problem from the banking
                 sector with encouraging results.",
}

Genetic Programming entries for Athanasios D Tsakonas Georgios Dounias

Citations