Data Mining using Genetic Programming: Classification and Symbolic Regression

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

@PhdThesis{eggermont:thesis,
  author =       "Jeroen Eggermont",
  title =        "Data Mining using Genetic Programming: Classification
                 and Symbolic Regression",
  school =       "Institute for Programming research and Algorithmics,
                 Leiden Institute of Advanced Computer Science, Faculty
                 of Mathematics \& Natural Sciences, Leiden University",
  year =         "2005",
  address =      "The Netherlands",
  month =        "14 " # sep,
  bibsource =    "OAI-PMH server at openaccess.leidenuniv.nl",
  contributor =  "Jeroen Eggermont",
  description =  "Promotor: Prof. dr. J.N. Kok. Co-promotor: Dr. W.A.
                 Kosters. Referent: Dr. W.B. Langdon.; With Summary in
                 Dutch.",
  format =       "29005 bytes; 685481 bytes",
  identifier =   "Eggermont, J., 2005. Doctoral Thesis, Leiden
                 University; 90-9019760-5",
  language =     "en",
  relation =     "IPA Dissertation Series;2005-12",
  keywords =     "genetic algorithms, genetic programming, data mining",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.155.3989",
  URL =          "https://openaccess.leidenuniv.nl/dspace/bitstream/1887/3393/1/proefschriftppi-eggermont.pdf",
  ISBN =         "90-90-19760-5",
  size =         "179 pages",
  abstract =     "Sir Francis Bacon said about four centuries ago:
                 {"}Knowledge is Power{"}. If we look at today's
                 society, information is becoming increasingly
                 important. According to [73] about five exabytes (5 x
                 1018 bytes) of new information were produced in 2002,
                 92% of which on magnetic media (e.g., hard-disks). This
                 was more than double the amount of information produced
                 in 1999 (2 exabytes). However, as Albert Einstein
                 observed: {"}Information is not Knowledge{"}.

                 One of the challenges of the large amounts of
                 information stored in databases is to find or extract
                 potentially useful, understandable and novel patterns
                 in data which can lead to new insights. To quote T.S.
                 Eliot: {"}Where is the knowledge we have lost in
                 information ?{"} [35]. This is the goal of a process
                 called Knowledge Discovery in Databases (KDD) [36]. The
                 KDD process consists of several phases: in the Data
                 Mining phase the actual discovery of new knowledge
                 takes place.

                 The outline of the rest of this introduction is as
                 follows. We start with an introduction of Data Mining
                 and more specifically the two subject areas of Data
                 Mining we will be looking at: classification and
                 regression. Next we give an introduction about
                 evolutionary computation in general and tree-based
                 genetic programming in particular. In Section 1.4 we
                 give our motivation for using genetic programming for
                 Data Mining. Finally, in the last sections we give an
                 overview of the thesis and related publications.",
  notes =        "IPA 1887/3393",
}

Genetic Programming entries for Jeroen Eggermont

Citations