Exploring the underlying structure of haptic-based handwritten signatures using visual data mining techniques

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

@InProceedings{Sakr:2010:ieeeHaptics,
  author =       "Nizar Sakr and Fawaz A. Alsulaiman and 
                 Julio J. Valdes and Abdulmotaleb El Saddik and Nicolas D. Georganas",
  title =        "Exploring the underlying structure of haptic-based
                 handwritten signatures using visual data mining
                 techniques",
  booktitle =    "2010 IEEE Haptics Symposium",
  year =         "2010",
  month =        "25-26 " # mar,
  pages =        "467--474",
  abstract =     "In this paper, multidimensional and time-varying
                 haptic-based handwritten signatures are analysed within
                 a visual data mining paradigm while relying on
                 unsupervised construction of virtual reality spaces
                 using classical optimisation and genetic programming.
                 Specifically, the suggested approaches make use of
                 nonlinear transformations to map a high dimensional
                 feature space into another space of smaller dimension
                 while minimising some error measure of information
                 loss. A comparison between genetic programming and
                 classical optimisation techniques in the construction
                 of visual spaces using large haptic datasets, is
                 provided. In addition, different distance functions
                 (used in the nonlinear mapping procedure between the
                 original and visual spaces) are examined to explore
                 whether the choice of measure affects the
                 representation accuracy of the computed visual spaces.
                 Furthermore, different classifiers (Support Vector
                 Machines (SVM), k-nearest neighbours (k-NN), and Naive
                 Bayes) are exploited in order to evaluate the potential
                 discrimination power of the generated attributes. The
                 results show that the relationships between the haptic
                 data objects and their classes can be appreciated in
                 most of the obtained spaces regardless of the mapping
                 error. Also, spaces computed using classical
                 optimization resulted in lower mapping errors and
                 better discrimination power than genetic programming,
                 but the later provides explicit equations relating the
                 original and the new spaces.",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, Naive Bayes, classical
                 optimization techniques, distance functions,
                 information loss, k-nearest neighbors, multidimensional
                 haptic-based handwritten signatures, nonlinear mapping,
                 support vector machines, time-varying haptic-based
                 handwritten signatures, virtual reality spaces, visual
                 data mining techniques, data mining, haptic interfaces,
                 pattern classification, support vector machines,
                 virtual reality",
  DOI =          "doi:10.1109/HAPTIC.2010.5444614",
  notes =        "Distrib. & Collaborative Virtual Environments Res.
                 Lab., Univ. of Ottawa, Ottawa, ON, Canada Also known as
                 \cite{5444614}",
}

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

Citations