Combination and optimization of classifiers in gender classification using genetic programming

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@Article{Khan:2005:IJKBIE,
  author =       "Asifullah Khan and Abdul Majid and Anwar M. Mirza",
  title =        "Combination and optimization of classifiers in gender
                 classification using genetic programming",
  journal =      "International Journal of Knowledge-Based and
                 Intelligent Engineering Systems",
  year =         "2005",
  volume =       "9",
  number =       "1",
  pages =        "1--11",
  keywords =     "genetic algorithms, genetic programming, gender
                 classification, principal component analysis,
                 eigenface, jackknife technique, receiver operating
                 characteristics curve, area under the convex hull,
                 AUROC",
  ISSN =         "1327-2314",
  URL =          "http://content.iospress.com/articles/international-journal-of-knowledge-based-and-intelligent-engineering-systems/kes00019",
  DOI =          "doi:10.3233/KES-2005-9101",
  size =         "11 pages",
  abstract =     "we have investigated the problem of gender
                 classification using frontal facial images. Four
                 different classifiers, namely K-means, k-nearest
                 neighbours, Linear Discriminant Analysis and
                 Mahalanobis Distance Based classifiers are compared.
                 Receiver operating characteristics (ROC) curve along
                 with the area under the convex hull (AUCH) have been
                 used as the performance measures of the classifiers at
                 different feature subsets. To measure the overall
                 performance of a classifier with single scalar value,
                 the new scheme of finding the area under the convex
                 hull of AUCH of ROC curves (AUCH of AUCHS) is proposed.
                 It has been observed that, when the number of macro
                 features is increased beyond 5, the AUCH saturates and
                 even decreases for some classifiers, illustrating the
                 curse of dimensionality. We then used genetic
                 programming to combine classifiers and thus evolved an
                 optimum combined classifier (OCC), producing better
                 performance than the individual classifiers. We found
                 that using only two features, the OCC has comparable
                 performance to that of original classifier using 20
                 macro features. It produces true positive rate values
                 as high as 0.94 corresponding to false positive rate as
                 low as 0.15 for 1: 3 train to testing ratio. We also
                 observed that heterogeneous combination of classifiers
                 is more promising than the homogenous combination.",
}

Genetic Programming entries for Asifullah Khan Abdul Majid Anwar M Mirza

Citations