The Application of Genetic Programming For Feature Construction in Classification

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

@PhdThesis{Muharram:thesis,
  author =       "Mohammed Ahmed Yahya Muharram",
  title =        "The Application of Genetic Programming For Feature
                 Construction in Classification",
  school =       "School of Computing Sciences at the University of East
                 Anglia",
  year =         "2005",
  address =      "Norwich, England",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/Muharram_thesis.pdf",
  URL =          "https://ueaeprints.uea.ac.uk/3801/",
  URL =          "http://ethos.bl.uk/OrderDetails.do?did=22&uin=uk.bl.ethos.426687",
  size =         "187 pages",
  abstract =     "This Thesis addresses the task of feature construction
                 for classification. The quality of the data is one of
                 the most important factors influencing the performance
                 of any classification algorithm. The attributes
                 defining the feature space of a given data set can
                 often be inadequate, making it difficult to discover
                 interesting knowledge. However, even when the original
                 attributes are individually inadequate, it is often
                 possible to combine such attributes in order to
                 construct new ones with greater predictive power.

                 The goal of this Thesis is to restructure the feature
                 space in order to improve the performance of decision
                 tree classification techniques on complex, real world
                 data. The proposed framework involves the use of
                 genetic programming to evolve (construct) new
                 attributes, which are non-linear combinations of the
                 original attributes. This approach incorporates a
                 number of decision tree splitting mechanisms in the
                 fitness measures of the genetic program.

                 The empirical results obtained are encouraging and show
                 that classification techniques can definitely benefit
                 from the inclusion of an evolved attribute in terms of
                 the accuracy and model size (for decision tree
                 classifiers). When compared to existing approaches, the
                 use of a decision tree splitting criteria as the
                 fitness of the genetic program prove to be competitive
                 and robust in terms predictive accuracy. Additionally,
                 some of the evolved attributes manage to uncover
                 physical properties in the data.",
  notes =        "

                 Evolving new more predictive features for a number of
                 classification techniques, particularly, decision tree
                 classification algorithms.

                 The GP incorporates the splitting mechanism of a
                 decision tree classifier as its fitness for
                 constructing new features.

                 pages3-4

                 Full text not available from this repository
                 uk.bl.ethos.426687",
}

Genetic Programming entries for Mohammed A Muharram

Citations