Enhancing Knowledge Discovery via Association-Based Evolution of Neural Logic Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Henry W. K. Chia and Chew Lim Tan and Sam Y. Sung",
  title =        "Enhancing Knowledge Discovery via Association-Based
                 Evolution of Neural Logic Networks",
  journal =      "IEEE Transactions on Knowledge and Data Engineering",
  volume =       "18",
  number =       "7",
  year =         "2006",
  publisher =    "IEEE Computer Society",
  address =      "Los Alamitos, CA, USA",
  pages =        "889--901",
  keywords =     "genetic algorithms, genetic programming, Data mining,
                 knowledge acquisition, connectionism and neural nets,
                 rule-based knowledge representation",
  ISSN =         "1041-4347",
  DOI =          "doi:10.1109/TKDE.2006.111",
  abstract =     "The comprehensibility aspect of rule discovery is of
                 emerging interest in the realm of knowledge discovery
                 in databases. Of the many cognitive and psychological
                 factors relating the comprehensibility of knowledge, we
                 focus on the use of human amenable concepts as a
                 representation language in expressing classification
                 rules. Existing work in neural logic networks (or
                 neulonets) provides impetus for our research; its
                 strength lies in its ability to learn and represent
                 complex human logic in decision-making using
                 symbolic-interpretable net rules. A novel technique is
                 developed for neulonet learning by composing net rules
                 using genetic programming. Coupled with a sequential
                 covering approach for generating a list of neulonets,
                 the straightforward extraction of human-like logic
                 rules from each neulonet provides an alternate
                 perspective to the greater extent of knowledge that can
                 potentially be expressed and discovered, while the
                 entire list of neulonets together constitute an
                 effective classifier. We show how the sequential
                 covering approach is analogous to association-based
                 classification, leading to the development of an
                 association-based neulonet classifier. Empirical study
                 shows that associative classification integrated with
                 the genetic construction of neulonets performs better
                 than general association-based classifiers in terms of
                 higher accuracies and smaller rule sets. This is due to
                 the richness in logic expression inherent in the
                 neulonet learning paradigm.",

Genetic Programming entries for Henry Wai-Kit Chia Chew-Lim Tan Sam Y Sung