Polynomial-fuzzy decision tree structures for classifying medical data

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "E. M. Mugambi and Andrew Hunter and Giles Oatley and 
                 Lee Kennedy",
  title =        "Polynomial-fuzzy decision tree structures for
                 classifying medical data",
  journal =      "Knowledge-Based Systems",
  year =         "2004",
  volume =       "17",
  pages =        "81--87",
  number =       "2-4",
  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.",
  owner =        "wlangdon",
  URL =          "http://www.sciencedirect.com/science/article/B6V0P-4C4VYG9-2/2/8ee7c8541e99bf3c8c22922dad2ebfbf",
  keywords =     "genetic algorithms, genetic programming, Decision
                 tree, Comprehensibility, Performance, Multiobjective
                 genetic programming",
  DOI =          "doi:10.1016/j.knosys.2004.03.003",

Genetic Programming entries for Ernest Mugambi Andrew Hunter Giles Oatley Lee Kennedy