Improving Performance of Nearest Neighborhood Classifier Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Abdul Majid and Asifullah Khan and Anwar M. Mirza",
  title =        "Improving Performance of Nearest Neighborhood
                 Classifier Using Genetic Programming",
  booktitle =    "The Third International Conference on Machine Learning
                 and Applications (ICMLA-04)",
  year =         "2004",
  pages =        "469--476",
  address =      "Louisville, KY, USA",
  month =        "16-18 " # dec,
  organisation = "IEEE/ACM",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/ICMLA.2004.1383552",
  size =         "8 pages",
  abstract =     "Nearest neighbourhood classifier (kNN) is most widely
                 used in pattern recognition applications. Depending on
                 the selection of voting methodology, the problem of
                 outliers has been encountered in this classifier.
                 Therefore, selection and optimisation of the voting
                 methodology is very important. In this work, we have
                 used Genetic Programming (GP) to improve the
                 performance of nearest neighbour classifier. Instead of
                 using predefined k nearest neighbors, the number of men
                 and women in the first two quartiles in Euclidean space
                 are used for voting. GP is, then, used to evolve an
                 optimal class mapping function that effectively reduces
                 the outliers. The performance of modified nearest
                 neighborhood (ModNN) classifier is then compared with
                 the conventional kNN for gender classification problem.
                 Receiver Operating Characteristics curve and its Area
                 Under the Convex Hull (A UCH) are used as the
                 performance measures. Considering the first three and
                 first five eigen features respectively, ModNN achieves
                 AUCH equal to 0.985 and 0.992 as compared to 0.9693 and
                 0.9795 of conventional kNN respectively",
  notes =        "Broken Jan 2013
        also known as

Genetic Programming entries for Abdul Majid Asifullah Khan Anwar M Mirza