Discovery of Mineralization Predication Classification Rules by Using Gene Expression Programming Based on PCA

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

@InProceedings{Zhang:2009:ICNC,
  title =        "Discovery of Mineralization Predication Classification
                 Rules by Using Gene Expression Programming Based on
                 PCA",
  author =       "Dongmei Zhang and Yue Huang and Jing Zhi",
  booktitle =    "Fifth International Conference on Natural Computation,
                 2009. ICNC '09",
  year =         "2009",
  pages =        "540--543",
  editor =       "Haiying Wang and Kay Soon Low and Kexin Wei and 
                 Junqing Sun",
  month =        "14-16 " # aug,
  address =      "Tianjian, China",
  publisher =    "IEEE Computer Society",
  isbn13 =       "978-0-7695-3736-8",
  bibdate =      "2010-01-22",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/icnc/icnc2009-4.html#ZhangHZ09",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  DOI =          "doi:10.1109/ICNC.2009.367",
  abstract =     "Classification is one of the fundamental tasks in
                 geology field. In this paper, we propose an
                 evolutionary approach for discovering classification
                 rules of mineralization predication from distinct
                 combinations of geochemistry elements by using gene
                 expression programming (GEP). The innovative part of
                 the paper presents integrated/hybrid model-combine GEP
                 evolution modeling with Principal Component Analysis
                 (PCA), which reduce multidimensional data sets. Mineral
                 deposit with tin and copper in Gejiu is chosen as the
                 research area. MAPGIS and MORPAS are used to extract
                 the value of ore-controlled factors by mapping geologic
                 maps into grid cell. Case study illustrates the
                 proposed GEP approach Based on PCA is more efficient
                 and accurate in a large searching space, compared with
                 Decision Tree (C4.5) and Bayesian Networks.",
  notes =        "Also known as \cite{5367093}",
}

Genetic Programming entries for Dongmei Zhang Yue Huang Jing Zhi

Citations