Towards simultaneous prototype and Feature Generation

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Mauricio {Garcia Limon} and Hugo Jair Escalante and 
                 Eduardo F. Morales",
  booktitle =    "IEEE International Autumn Meeting on Power,
                 Electronics and Computing (ROPEC 2014)",
  title =        "Towards simultaneous prototype and Feature
  year =         "2014",
  month =        nov,
  abstract =     "Nearest-neighbour (NN) methods are among the most
                 popular and highly effective techniques used in pattern
                 recognition tasks. However, these methods have several
                 drawbacks that impair their performance in large scale
                 problems and noisy data sets. Some of these
                 disadvantages includes its high storage requirements,
                 its sensitivity to noisy instances, and the
                 computational cost for estimating the distance among
                 all instances. To address these problems different
                 techniques like Prototype Generation (PG) to reduce the
                 number of instances, and Feature Generation (FG) to
                 obtain a new set of features have been proposed;
                 traditionally, both techniques have been applied
                 separately. This paper introduces a new method for
                 simultaneous generation of prototypes and features in
                 order to obtain a good tirade between accuracy of
                 classification with a NN classifier, instance reduction
                 rate and feature reduction rate. The method presented
                 is based on the algorithm NSGA-II; the main idea of the
                 proposed method is to combine instances and attributes
                 to produce a set of prototypes and a new feature space
                 for each class of the classification problem via
                 genetic programming. The proposed approach overcomes
                 some limitations of NN without compromising its
                 performance in classification task. Experimental
                 results are reported and compared with several other
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/ROPEC.2014.7036346",
  notes =        "Inst. Nac. de Astrofis. Opt. y Electron.,
                 Tonantzintla, Mexico

                 Also known as \cite{7036346}",

Genetic Programming entries for Mauricio Garcia-Limon Hugo Jair Escalante Eduardo F Morales