Lattice-based clustering and genetic programming for coordinate transformation in GPS applications

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Chih-Hung Wu and Wei-Han Su",
  title =        "Lattice-based clustering and genetic programming for
                 coordinate transformation in {GPS} applications",
  journal =      "Computer \& Geosciences",
  volume =       "52",
  pages =        "85--94",
  year =         "2013",
  keywords =     "genetic algorithms, genetic programming, Clustering,
                 Symbolic regression, GPS, Lattices, Coordinate
  ISSN =         "0098-3004",
  DOI =          "doi:10.1016/j.cageo.2012.09.022",
  URL =          "",
  abstract =     "Coordinate transformation is essential in many
                 georeferencing applications. Level-wised transformation
                 can be considered as a regression problem and done by
                 machine-learning approaches. However, inaccurate and
                 biased results are usually derived when training data
                 do not uniformly distribute. In this paper, the
                 performance of regression-based coordinate
                 transformation for GPS applications is discussed. A
                 lattice-based clustering method is developed and
                 integrated with genetic programming for building better
                 regression models of coordinate transformation. The GPS
                 application area is first partitioned into lattices
                 with lattice sizes being determined by the geographic
                 locations and distribution of the GPS reference points.
                 Clustering is then performed on lattices, not on data
                 points. Each cluster of lattices serves as a training
                 data set for a genetic regression model of coordinate
                 transformation. In this manner, the data points
                 contained in the different lattices can be considered
                 to be of the same importance. Biased regression results
                 caused by the imbalanced distribution of data can also
                 be eliminated. The experimental results show that the
                 proposed method can further improve the positioning
                 accuracy than previous methods.",

Genetic Programming entries for Chih-Hung Wu Wei-Han Su