Feature selection and classification in genetic programming: Application to haptic-based biometric data

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

  author =       "Fawaz A. Alsulaiman and Nizar Sakr and 
                 Julio J. Valdes and Abdulmotaleb {El Saddik} and Nicolas D. Georganas",
  title =        "Feature selection and classification in genetic
                 programming: Application to haptic-based biometric
  booktitle =    "IEEE Symposium on Computational Intelligence for
                 Security and Defense Applications, CISDA 2009",
  year =         "2009",
  month =        jul,
  pages =        "1--7",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, analytic function,
                 dimensionality reducers, feature selection, haptic
                 dataset, haptic-based biometric data, haptic-based
                 biometrics problem, high-dimensional haptic feature
                 space, perfect classification model, feature
                 extraction, haptic interfaces, pattern classification",
  DOI =          "doi:10.1109/CISDA.2009.5356540",
  abstract =     "In this paper, a study is conducted in order to
                 explore the use of genetic programming, in particular
                 gene expression programming (GEP), in finding analytic
                 functions that can behave as classifiers in
                 high-dimensional haptic feature spaces. More
                 importantly, the determined explicit functions are used
                 in discovering minimal knowledge-preserving subsets of
                 features from very high dimensional haptic datasets,
                 thus acting as general dimensionality reducers. This
                 approach is applied to the haptic-based biometrics
                 problem; namely, in user identity verification. GEP
                 models are initially generated using the original
                 haptic biometric datatset, which is imbalanced in terms
                 of the number of representative instances of each
                 class. This procedure was repeated while considering an
                 under-sampled (balanced) version of the datasets. The
                 results demonstrated that for all datasets, whether
                 imbalanced or under-sampled, a certain number (on
                 average) of perfect classification models were
                 determined. In addition, using GEP, great feature
                 reduction was achieved as the generated analytic
                 functions (classifiers) exploited only a small fraction
                 of the available features.",
  notes =        "Also known as \cite{5356540}",

Genetic Programming entries for Fawaz A Alsulaiman Nizar Sakr Julio J Valdes Abdulmotaleb El Saddik Nicolas D Georganas