Improved genetic programming techniques for data classification

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

@PhdThesis{Al-Madi:thesis,
  author =       "Nailah Shikri Al-Madi",
  title =        "Improved genetic programming techniques for data
                 classification",
  school =       "Computer Science, North Dakota State University",
  year =         "2013",
  address =      "Fargo, North Dakota, USA",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, Artificial
                 intelligence, Computer science, Applied sciences, Data
                 classification, Data mining, MRGP",
  URL =          "http://gradworks.umi.com/36/14/3614489.html",
  URL =          "http://search.proquest.com/docview/1518147523",
  size =         "123 pages",
  abstract =     "Evolutionary algorithms are one category of
                 optimisation techniques that are inspired by processes
                 of biological evolution. Evolutionary computation is
                 applied to many domains and one of the most important
                 is data mining. Data mining is a relatively broad field
                 that deals with the automatic knowledge discovery from
                 databases and it is one of the most developed fields in
                 the area of artificial intelligence. Classification is
                 a data mining method that assigns items in a collection
                 to target classes with the goal to accurately predict
                 the target class for each item in the data. Genetic
                 programming (GP) is one of the effective evolutionary
                 computation techniques to solve classification
                 problems. GP solves classification problems as an
                 optimization tasks, where it searches for the best
                 solution with highest accuracy. However, GP suffers
                 from some weaknesses such as long execution time, and
                 the need to tune many parameters for each problem.
                 Furthermore, GP can not obtain high accuracy for
                 multiclass classification problems as opposed to binary
                 problems. In this dissertation, we address these
                 drawbacks and propose some approaches in order to
                 overcome them. Adaptive GP variants are proposed in
                 order to automatically adapt the parameter settings and
                 shorten the execution time. Moreover, two approaches
                 are proposed to improve the accuracy of GP when applied
                 to multiclass classification problems. In addition, a
                 Segment-based approach is proposed to accelerate the GP
                 execution time for the data classification problem.
                 Furthermore, a parallelisation of the GP process using
                 the MapReduce methodology was proposed which aims to
                 shorten the GP execution time and to provide the
                 ability to use large population sizes leading to a
                 faster convergence. The proposed approaches are
                 evaluated using different measures, such as accuracy,
                 execution time, sensitivity, specificity, and
                 statistical tests. Comparisons between the proposed
                 approaches with the standard GP, and with other
                 classification techniques were performed, and the
                 results showed that these approaches overcome the
                 drawbacks of standard GP by successfully improving the
                 accuracy and execution time.",
  notes =        "Advisor: Simone A. Ludwig ProQuest, UMI Dissertations
                 Publishing, 2014. 3614489",
}

Genetic Programming entries for Nailah Al-Madi

Citations