Polynomial-fuzzy decision tree structures for classifying medical data
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @Article{Mugambi:2004:KBS,
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author = "E. M. Mugambi and Andrew Hunter and Giles Oatley and
Lee Kennedy",
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title = "Polynomial-fuzzy decision tree structures for
classifying medical data",
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journal = "Knowledge-Based Systems",
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year = "2004",
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volume = "17",
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pages = "81--87",
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number = "2-4",
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month = may,
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keywords = "genetic algorithms, genetic programming, Decision
tree, Comprehensibility, Performance, Multiobjective
genetic programming",
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ISSN = "0950-7051",
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broken = "http://www.sciencedirect.com/science/article/B6V0P-4C4VYG9-2/2/8ee7c8541e99bf3c8c22922dad2ebfbf",
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DOI = "doi:10.1016/j.knosys.2004.03.003",
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owner = "wlangdon",
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abstract = "Decision tree induction has been studied extensively
in machine learning as a solution for classification
problems. The way the linear decision trees partition
the search space is found to be comprehensible and
hence appealing to data modellers. Comprehensibility is
an important aspect of models used in medical data
mining as it determines model credibility and even
acceptability. In the practical sense though,
inordinately long decision trees compounded by
replication problems detracts from comprehensibility.
This demerit can be partially attributed to their rigid
structure that is unable to handle complex non-linear
or/and continuous data. To address this issue we
introduce a novel hybrid multivariate decision tree
composed of polynomial, fuzzy and decision tree
structures. The polynomial nature of these multivariate
trees enable them to perform well in non-linear
territory while the fuzzy members are used to squash
continuous variables. By trading-off comprehensibility
and performance using a multi-objective genetic
programming optimisation algorithm, we can induce
polynomial-fuzzy decision trees (PFDT) that are
smaller, more compact and of better performance than
their linear decision tree (LDT) counterparts. we
discuss the structural differences between PFDT and LDT
(C4.5) and compare the size and performance of their
models using medical data.",
- }
Genetic Programming entries for
Ernest Mugambi
Andrew Hunter
Giles Oatley
R Lee Kennedy
Citations