Soft decision trees: A genetically optimized cluster oriented approach
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- @Article{Shukla2009551,
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author = "Sanjay Kumar Shukla and M. K. Tiwari",
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title = "Soft decision trees: A genetically optimized cluster
oriented approach",
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journal = "Expert Systems with Applications",
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volume = "36",
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number = "1",
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pages = "551--563",
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year = "2009",
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ISSN = "0957-4174",
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DOI = "doi:10.1016/j.eswa.2007.09.065",
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URL = "http://www.sciencedirect.com/science/article/B6V03-4PYGVT6-4/2/e3c4d3158b51068a88cddfd1368d1503",
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keywords = "genetic algorithms, genetic programming, Decision
trees, Fuzzy clustering, Inconsistency index",
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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.",
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notes = "GA used to evolve variable sized trees",
- }
Genetic Programming entries for
Sanjay Kumar Shukla
Manoj Kumar Tiwari
Citations