Self-adjusting Associative Rules Generator for Classification : An Evolutionary Computation Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Lavangnananda:2006:ieeeMWALS,
  author =       "K. Lavangnananda",
  title =        "Self-adjusting Associative Rules Generator for
                 Classification : An Evolutionary Computation Approach",
  booktitle =    "2006 IEEE Mountain Workshop on Adaptive and Learning
                 Systems",
  year =         "2006",
  pages =        "237--242",
  address =      "Logan, UT, USA",
  month =        "24-26 " # jul,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-4244-0166-6",
  URL =          "http://dummy/Lavangnananda_2006_ieeeMWALS.pdf",
  DOI =          "doi:10.1109/SMCALS.2006.250722",
  size =         "6 pages",
  abstract =     "The problem of generating efficient association rules
                 can seen as search problem since many different sets of
                 rules are possible from a given set of instances. As
                 the application of evolutionary computation in
                 searching is well studied, it is possible to use
                 evolutionary computation in mining for efficient
                 association rules. In this paper, a program known as
                 self-adjusting associative rules generator (SARG) is
                 described. SARG is a data mining program which can
                 generate associative rules for classification. It is an
                 improvement of the data mining program called genetic
                 programming for inductive learning (GPIL). Both use
                 evolutionary computation in inductive learning. The
                 shortcoming of GPIL lies in the operations crossover
                 and selection. These two operations were inflexible and
                 not able to adjust themselves in order to select
                 suitable methods for the task at hand. SARG introduces
                 new method of crossover known as MaxToMin crossover
                 together with a self-adjusting reproduction. It has
                 been tested on several benchmark data sets available in
                 the public domain. Comparison between GPIL and SARG
                 revealed that SARG achieved better performance and was
                 able to classify these data sets with higher accuracy.
                 The paper also discusses relevant aspects of SARG and
                 suggests directions for future work",
  notes =        "Title should have been: {"}Self-adjusting Association
                 Rules Generator for Classification : An Evolutionary
                 Computation Approach{"}

                 INSPEC Accession Number: 9131818

                 Sch. of Inf. Technol., King Mongkut's Inst. of
                 Technol., Bangkok;",
}

Genetic Programming entries for Kittichai Lavangnananda

Citations