Evolving Compact Decision Rule Sets

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

@Book{Marmelstein:book,
  author =       "Robert E. Marmelstein",
  title =        "Evolving Compact Decision Rule Sets",
  publisher =    "Storming Media",
  year =         "1999",
  address =      "USA",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-4235-4475-7",
  isbn13 =       "978-1288324286",
  URL =          "https://www.amazon.co.uk/Evolving-Compact-Decision-Rule-Sets/dp/1288324286",
  URL =          "http://www.brightsurf.com/brightsurf/books/1423544757/Evolving_Compact_Decision_Rule_Sets.html",
  URL =          "https://www.abebooks.co.uk/servlet/SearchResults?bi=0&bx=off&ds=30&isbn=9781288324286&recentlyadded=all&sortby=17&sts=t",
  abstract =     "This is a AIR FORCE INSTITUTE OF TECHNOLOGY
                 WRIGHT-PATTERSON Air Force Base OH report procured by
                 the Pentagon and made available for public release. It
                 has been reproduced in the best form available to the
                 Pentagon. It is not spiral-bound, but rather assembled
                 with Velobinding in a soft, white linen cover. The
                 Storming Media report number is A932463. The abstract
                 provided by the Pentagon follows: 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 fits the data, the rules found by GRaCCE are
                 of greater use than those identified by existing
                 methods.",
  notes =        "Spiral-bound ?

                 Oct 2016 Appears to have been republished by
                 BiblioScholar (21 Nov. 2012) ISBN-13: 978-1288324286",
}

Genetic Programming entries for Robert Evan Marmelstein

Citations