Dimensionality reduction in face detection: A genetic programming approach

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

@InProceedings{Neshatian:2009:IVCNZ,
  title =        "Dimensionality reduction in face detection: A genetic
                 programming approach",
  author =       "Kourosh Neshatian and Mengjie Zhang",
  year =         "2009",
  pages =        "391--396",
  booktitle =    "Proceeding of the 24th International Conference Image
                 and Vision Computing New Zealand, IVCNZ '09",
  month =        "23-25 " # nov,
  address =      "Wellington",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4244-4697-1",
  ISSN =         "2151-2205",
  DOI =          "doi:10.1109/IVCNZ.2009.5378375",
  abstract =     "The high number of features in many machine vision
                 applications has a major impact on the performance of
                 machine learning algorithms. Feature selection (FS) is
                 an avenue to dimensionality reduction. Evolutionary
                 search techniques have been very promising in finding
                 solutions in the exponentially growing search space of
                 FS problems. This paper proposes a genetic programming
                 (GP) approach to FS where the building blocks are
                 subsets of features and set operators. We use bit-mask
                 representation for subsets and a set of set operators
                 as primitive functions. The GP search, then combines
                 these subsets and set operations to find an optimal
                 subset of features. The task we study is a highly
                 imbalanced face detection problem. A modified version
                 of the Naive Bayes classification model is used as the
                 fitness function. Our results show that the proposed
                 algorithm can achieve a significant reduction in
                 dimensionality and processing time. Using the
                 GP-selected features, the performance of certain
                 classifiers can also be improved.",
  notes =        "Also known as \cite{5378375}",
}

Genetic Programming entries for Kourosh Neshatian Mengjie Zhang

Citations