Distance Guided Classification with Gene Expression Programming

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  title =        "Distance Guided Classification with Gene Expression
  author =       "Lei Duan and Changjie Tang and Tianqing Zhang and 
                 Dagang Wei and Huan Zhang",
  booktitle =    "Advanced Data Mining and Applications, Proceedings of
                 the Second International Conference, {ADMA}",
  publisher =    "Springer",
  year =         "2006",
  volume =       "4093",
  editor =       "Xue Li and Osmar R. Za{\"i}ane and Zhanhuai Li",
  pages =        "239--246",
  series =       "Lecture Notes in Computer Science",
  address =      "Xi'an, China",
  month =        aug # " 14-16",
  bibdate =      "2006-08-21",
  bibsource =    "DBLP,
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming",
  ISBN =         "3-540-37025-0",
  DOI =          "doi:10.1007/11811305_26",
  abstract =     "Gene Expression Programming (GEP) aims at discovering
                 essential rules hidden in observed data and expressing
                 them mathematically. GEP has been proved to be a
                 powerful tool for constructing efficient classifiers.
                 Traditional GEP-classifiers ignore the distribution of
                 samples, and hence decrease the efficiency and
                 accuracy. The contributions of this paper include: (1)
                 proposing two strategies of generating classification
                 threshold dynamically, (2) designing a new approach
                 called Distance Guided Evolution Algorithm (DGEA) to
                 improve the efficiency of GEP, and (3) demonstrating
                 the effectiveness of generating classification
                 threshold dynamically and DGEA by extensive
                 experiments. The results show that the new methods
                 decrease the number of evolutional generations by
                 83percent to 90percent, and increase the accuracy by
                 20percent compared with the traditional approach.",

Genetic Programming entries for Lei Duan Changjie Tang Tianqing Zhang Dagang Wei Huan Zhang