Soft decision trees: A genetically optimized cluster oriented approach

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@Article{Shukla2009551,
  author =       "Sanjay Kumar Shukla and M. K. Tiwari",
  title =        "Soft decision trees: A genetically optimized cluster
                 oriented approach",
  journal =      "Expert Systems with Applications",
  volume =       "36",
  number =       "1",
  pages =        "551--563",
  year =         "2009",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2007.09.065",
  URL =          "http://www.sciencedirect.com/science/article/B6V03-4PYGVT6-4/2/e3c4d3158b51068a88cddfd1368d1503",
  keywords =     "genetic algorithms, genetic programming, Decision
                 trees, Fuzzy clustering, Inconsistency index",
  abstract =     "When descriptions of data values are too detailed, the
                 computational complexities involved in mining useful
                 knowledge from the database generally increases. This
                 gives rise to the need of tools techniques which can
                 reduce these complexities and mine the valuable
                 information hidden behind the database. There exists
                 number of such techniques viz. decision trees, neural
                 networks, rough-set theory, rule induction, and
                 case-based reasoning which are able to meet the
                 aforesaid objective up to some extent. Each of these
                 techniques has its advantages and limitations that
                 motivate researchers to develop new tools for the
                 mining tasks. In this paper, we have developed a novel
                 methodology, genetically optimised cluster oriented
                 soft decision trees (GCSDT), to glean vital information
                 embedded in the large databases. In contrast to the
                 standard C-fuzzy decision trees, where granules are
                 developed through fuzzy (soft) clustering, in the
                 proposed architecture granules are developed by means
                 of genetically optimised soft clustering. In the GCSDT
                 architecture, GA ameliorates the difficulty of choosing
                 an initialisation for the fuzzy clustering algorithm
                 and always avoids degenerate partitions. This provides
                 an effective means for the optimization of clustering
                 criterion, where an objective function can be
                 illustrated in terms of cluster's center. Growth of the
                 GCSDT is realised by expanding nodes of the tree,
                 characterised by the highest inconsistency index of the
                 information granules. In order to validate the proposed
                 tree structure it has been deployed on synthetic and
                 machine learning data sets. Moreover, Results are
                 compared with those produced by standard C4.5 decision
                 trees and C-fuzzy decision trees; further student
                 t-test is applied to show that these differences in
                 results are statistically significant.",
  notes =        "GA used to evolve variable sized trees",
}

Genetic Programming entries for Sanjay Kumar Shukla Manoj Kumar Tiwari

Citations