Multi-objective genetic programming for data visualization and classification

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@PhdThesis{Icke:thesis,
  author =       "Ilknur Icke",
  title =        "Multi-objective genetic programming for data
                 visualization and classification",
  school =       "Computer Science, City University of New York",
  year =         "2011",
  address =      "USA",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-267-01062-9",
  URL =          "http://files.matlabsite.com/docs/thesis/th930929283.pdf",
  URL =          "http://www.gc.cuny.edu/GC-Header/Alumni/Alumni-Dissertations-and-Theses.aspx?page=3&program=Computer+Science&searchterm=genetic%20programming&sortby=author",
  URL =          "http://dl.acm.org/citation.cfm?id=2395668",
  URL =          "http://phdtree.org/pdf/25930788-multi-objective-genetic-programming-for-data-visualization-and-classification/",
  size =         "239 pages",
  abstract =     "The process of knowledge discovery lies on a continuum
                 ranging between the human driven (manual exploration)
                 approaches to fully automatic data mining methods. As a
                 hybrid approach, the emerging field of visual analytics
                 aims to facilitate human-machine collaborative decision
                 making by providing automated analysis of data via
                 interactive visualizations. One area of interest in
                 visual analytics is to develop data transformation
                 methods that support visualization and analysis. In
                 this thesis, we develop an evolutionary computing based
                 multi-objective dimensionality reduction method for
                 visual data classification. The algorithm is called
                 Genetic Programming Projection Pursuit (G3P) where
                 genetic programming is used in order to automatically
                 create visualizations of higher dimensional labeled
                 datasets which are assessed in terms of discriminative
                 power and interpretability. We consider two forms of
                 interpretability of the visualizations: clearly
                 separated and compact class structures along with
                 easily interpretable data transformation expressions
                 relating the original data attributes to the
                 visualization axes. The G3P algorithm incorporates a
                 number of automated measures of interpretability that
                 were found to be in alignment with human judgement
                 through a user study we conducted.

                 On a number of data mining problems, we show that G3P
                 generates a large number of data transformations that
                 are better than those generated by a number of
                 dimensionality reduction methods such as the principal
                 components analysis (PCA), multiple discriminants
                 analysis (MDA) and targeted projection pursuit (TPP) in
                 terms of discriminative power and interpretability.",
  notes =        "UMI Number: 3481647

                 Supervisor Andrew Rosenberg",
}

Genetic Programming entries for Ilknur Icke

Citations