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  • 1.
    Hall, Mikael
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Technology and Media.
    CD-mapping derived from ultrasonic TSI-GM profiles2004Report (Other academic)
  • 2. Pavlenko, Tatjana
    et al.
    Hall, Mikael
    Rosen, Dietrich von
    Towards the optimal feature selection in high-dimensional bayesian network classifiers2004Report (Other academic)
    Abstract [en]

    Incorporating subset selection into a classification method often carries a number of advantages, especially when operating in the domain of high-dimensional features. In this paper, we focus on Bayesian network (BN) classifiers and formalize the feature selection from a perspective of improving classification accuracy. To exploring the effect of high-dimensionality we apply the

    growing dimension asymptotics, meaning that the number of training examples is relatively small compared to the number of feature nodes. In order to ascertain which set of features is indeed relevant for a classification task, we introduce a distance-based scoring measure

    reflecting how well the set separates different classes. This score is then employed to feature selection, using the weighted form of BN classifier. The idea is to view weights as inclusion-exclusion factors which eliminates the sets of features whose separation score do not exceed a given threshold. We establish the asymptotic optimal threshold and demonstrate that the proposed selection technique carries improvements over classification accuracy for different a priori assumptions concerning the separation strength.

  • 3.
    Pavlenko, Tatjana
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Engineering, Physics and Mathematics.
    Hall, Mikael
    von Rosen, D
    Andrushchenko, Z
    Towards the optimal feature selection in high-dimensional Bayesian network classifiers2004In: SOFT METHODOLOGY AND RANDOM INFORMATION SYSTEMS / [ed] LopezDiaz, M; Gil, MA; Grzegorzewski, P; Hryniewicz, O; Lawry, J, SPRINGER-VERLAG BERLIN , 2004, p. 613-620Conference paper (Refereed)
    Abstract [en]

    We focus on Bayesian network (BN) classifiers and formalize the feature selection from a perspective of improving classification accuracy. To exploring the effect of high-dimensionality we apply the growing dimension asymptotics. We modify the weighted BN by introducing inclusion-exclusion factors which eliminate the features whose separation score do not exceed a given threshold. We establish the asymptotic optimal threshold and demonstrate that the proposed selection technique carries improvements over classification accuracy.

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