Evolving Compact Decision Rule Sets

Created by W.Langdon from gp-bibliography.bib Revision:1.4448

  author =       "Robert Evan Marmelstein",
  title =        "Evolving Compact Decision Rule Sets",
  school =       "Faculty of the Graduate School of Engineering of the
                 Air Force Institute of Technology Air University",
  year =         "1999",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, GRaCCE,
  URL =          "ftp://math.chtf.stuba.sk/pub/vlado/thesis_Marmelstein/thesis_Marmelstein.pdf",
  URL =          "ftp://math.chtf.stuba.sk/pub/vlado/thesis_Marmelstein/thesis_Marmelstein.ps.gz",
  size =         "271 pages",
  abstract =     "With the increased proliferation of computing
                 equipment, there has been a corresponding explosion in
                 the number and size of databases. Although a great deal
                 of time and effort is spent building and maintaining
                 these databases, it is nonetheless rare that this
                 valuable resource is exploited to its fullest. The
                 principle reason for this paradox is that many
                 organizations lack the insight and/or expertise to
                 effectively translate this information into usable
                 knowledge. While data mining technology holds the
                 promise of automatically extracting useful patterns
                 (such as decision rules) from data, this potential has
                 yet to be realized. One of the major technical
                 impediments is that the current generation of data
                 mining tools produce decision rule sets that are very
                 accurate, but extremely complex and difficult to
                 interpret. As a result, there is a clear need for
                 methods that yield decision rule sets that are both
                 accurate and compact.

                 The development of the Genetic Rule and Classifier
                 Construction Environment (GRaCCE) is proposed as an
                 alternative to existing decision rule induction (DRI)
                 algorithms. GRaCCE is a multi-phase algorithm which
                 harnesses the power of evolutionary search to mine
                 classification rules from data. These rules are based
                 on piece-wise linear estimates of the Bayes decision
                 boundary within a winnowed subset of the data. Once a
                 sufficient set of these hyper-planes are generated, a
                 genetic algorithm (GA) based {"}0/1{"} search is
                 performed to locate combinations of them that enclose
                 class homogeneous regions of the data. It is shown that
                 this approach enables GRaCCE to produce rule sets
                 significantly more compact than those of other DRI
                 methods while achieving a comparable level of accuracy.
                 Since the principle of Occam's razor tells us to always
                 prefer the simplest model that its the data, the rules
                 found by GRaCCE are of greater utility than those
                 identified by existing methods.",
  notes =        "AFIT/DS/ENG/99-05 Approved for public release;
                 distribution unlimited Appendix B. GRaCCE User's

Genetic Programming entries for Robert Evan Marmelstein