Mining multiple comprehensible classification rules using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "K. C. Tan and A. Tay and T. H. Lee and C. M. Heng",
  title =        "Mining multiple comprehensible classification rules
                 using genetic programming",
  booktitle =    "Proceedings of the 2002 Congress on Evolutionary
                 Computation CEC2002",
  editor =       "David B. Fogel and Mohamed A. El-Sharkawi and 
                 Xin Yao and Garry Greenwood and Hitoshi Iba and Paul Marrow and 
                 Mark Shackleton",
  pages =        "1302--1307",
  year =         "2002",
  publisher =    "IEEE Press",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  organisation = "IEEE Neural Network Council (NNC), Institution of
                 Electrical Engineers (IEE), Evolutionary Programming
                 Society (EPS)",
  ISBN =         "0-7803-7278-6",
  month =        "12-17 " # may,
  notes =        "CEC 2002 - A joint meeting of the IEEE, the
                 Evolutionary Programming Society, and the IEE. Held in
                 connection with the World Congress on Computational
                 Intelligence (WCCI 2002)",
  keywords =     "genetic algorithms, genetic programming, IF-THEN rule
                 evolution, application domains, artificial immune
                 system-like memory vector, benchmark data sets, concept
                 mapping technique, covering algorithm, data mining,
                 fitness evaluation, multiple comprehensible
                 classification rules, redundant rule removal,
                 simulation, tree representation, data mining, pattern
                 classification, programming, redundancy",
  DOI =          "doi:10.1109/CEC.2002.1004431",
  abstract =     "Genetic Programming (GP) has been emerged as a
                 promising approach to deal with classification task in
                 data mining. This work extends the tree representation
                 of GP to evolve multiple comprehensible IF-THEN
                 classification rules. In the paper, we introduce a
                 concept mapping technique for fitness evaluation of
                 individuals. A covering algorithm that employs an
                 artificial immune system-like memory vector is used to
                 produce multiple rules as well as to remove redundant
                 rules. The proposed GP classifier is validated upon
                 nine benchmark datasets and the simulation results
                 confirm the viability and effectiveness of the GP
                 approach for solving data mining problems in a wide
                 spectrum of application domains.",
  notes =        "Michigan approach.

                 GPc, groovy Java GP (gjprog), WEKA. problem specific
                 population sizes 10-100 and w_1 and w_2.


Genetic Programming entries for Kay Chen Tan A Tay Tong Heng Lee C M Heng