Multi-Objective Genetic Programming for Visual Analytics

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Ilknur Icke and Andrew Rosenberg",
  title =        "Multi-Objective Genetic Programming for Visual
  booktitle =    "Proceedings of the 14th European Conference on Genetic
                 Programming, EuroGP 2011",
  year =         "2011",
  month =        "27-29 " # apr,
  editor =       "Sara Silva and James A. Foster and Miguel Nicolau and 
                 Mario Giacobini and Penousal Machado",
  series =       "LNCS",
  volume =       "6621",
  publisher =    "Springer Verlag",
  address =      "Turin, Italy",
  pages =        "322--334",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming: poster",
  DOI =          "doi:10.1007/978-3-642-20407-4_28",
  abstract =     "Visual analytics is a human-machine collaboration to
                 data modelling where extraction of the most informative
                 features plays an important role. Although feature
                 extraction is a multi-objective task, the traditional
                 algorithms either only consider one objective or
                 aggregate the objectives into one scalar criterion to
                 optimise. In this paper, we propose a Pareto-based
                 multi-objective approach to feature extraction for
                 visual analytics applied to data classification
                 problems. We identify classifiability, visual
                 interpretability and semantic interpretability as the
                 three equally important objectives for feature
                 extraction in classification problems and define
                 various measures to quantify these objectives. Our
                 results on a number of benchmark datasets show
                 consistent improvement compared to three standard
                 dimensionality reduction techniques. We also argue that
                 exploration of the multiple Pareto-optimal models
                 provide more insight about the classification problem
                 as opposed to a single optimal solution.",
  notes =        "Part of \cite{Silva:2011:GP} EuroGP'2011 held in
                 conjunction with EvoCOP2011 EvoBIO2011 and

Genetic Programming entries for Ilknur Icke Andrew Rosenberg