Confidence and Support Classification Using Genetically Programmed Neural Logic Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Henry Wai-Kit Chia and Chew-Lim Tan",
  title =        "Confidence and Support Classification Using
                 Genetically Programmed Neural Logic Networks",
  booktitle =    "Genetic and Evolutionary Computation -- GECCO-2004,
                 Part II",
  year =         "2004",
  editor =       "Kalyanmoy Deb and Riccardo Poli and 
                 Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and 
                 Paul Darwen and Dipankar Dasgupta and Dario Floreano and 
                 James Foster and Mark Harman and Owen Holland and 
                 Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and 
                 Dirk Thierens and Andy Tyrrell",
  series =       "Lecture Notes in Computer Science",
  pages =        "836--837",
  address =      "Seattle, WA, USA",
  publisher_address = "Heidelberg",
  month =        "26-30 " # jun,
  organisation = "ISGEC",
  publisher =    "Springer-Verlag",
  volume =       "3103",
  ISBN =         "3-540-22343-6",
  ISSN =         "0302-9743",
  DOI =          "doi:10.1007/b98645",
  URL =          "",
  size =         "2",
  keywords =     "genetic algorithms, genetic programming, Poster",
  abstract =     "Typical learning classifier systems employ conjunctive
                 logic rules for representing domain knowledge. The
                 classifier XCS is an extension of LCS with the ability
                 to learn boolean logic functions for data mining.
                 However, most data mining problems cannot be expressed
                 simply with boolean logic. Neural Logic Network
                 (Neulonet) learning is a technique that emulates the
                 complex human reasoning processes through the use of
                 net rules. Each neulonet is analogous to a learning
                 classifier that is rewarded using support and
                 confidence measures which are often used in
                 association-based classification. Empirical results
                 shows promise in terms of generalisation ability and
                 the comprehensibility of rules.",
  notes =        "GECCO-2004 A joint meeting of the thirteenth
                 international conference on genetic algorithms
                 (ICGA-2004) and the ninth annual genetic programming
                 conference (GP-2004)",

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