Adaptive Distance Metrics for Nearest Neighbour Classification based on Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Alexandros Agapitos and Michael O'Neill and 
                 Anthony Brabazon",
  title =        "Adaptive Distance Metrics for Nearest Neighbour
                 Classification based on Genetic Programming",
  booktitle =    "Proceedings of the 16th European Conference on Genetic
                 Programming, EuroGP 2013",
  year =         "2013",
  month =        "3-5 " # apr,
  editor =       "Krzysztof Krawiec and Alberto Moraglio and Ting Hu and 
                 A. Sima Uyar and Bin Hu",
  series =       "LNCS",
  volume =       "7831",
  publisher =    "Springer Verlag",
  address =      "Vienna, Austria",
  pages =        "1--12",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-37206-3",
  DOI =          "doi:10.1007/978-3-642-37207-0_1",
  abstract =     "Nearest Neighbour (NN) classification is a
                 widely-used, effective method for both binary and
                 multi-class problems. It relies on the assumption that
                 class conditional probabilities are locally constant.
                 However, this assumption becomes invalid in high
                 dimensions, and severe bias can be introduced, which
                 degrades the performance of the method. The employment
                 of a locally adaptive distance metric becomes crucial
                 in order to keep class conditional probabilities
                 approximately uniform, whereby better classification
                 performance can be attained. This paper presents a
                 locally adaptive distance metric for NN classification
                 based on a supervised learning algorithm (Genetic
                 Programming) that learns a vector of feature weights
                 for the features composing an instance query. Using a
                 weighted Euclidean distance metric, this has the effect
                 of adaptive neighbourhood shapes to query locations,
                 stretching the neighbourhood along the directions for
                 which the class conditional probabilities don't change
                 much. Initial empirical results on a set of real-world
                 classification datasets showed that the proposed method
                 enhances the generalisation performance of standard NN
                 algorithm, and that it is a competent method for
                 pattern classification as compared to other learning
  notes =        "Part of \cite{Krawiec:2013:GP} EuroGP'2013 held in
                 conjunction with EvoCOP2013, EvoBIO2013, EvoMusArt2013
                 and EvoApplications2013",

Genetic Programming entries for Alexandros Agapitos Michael O'Neill Anthony Brabazon