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Hall, Mikael
Publications (3 of 3) Show all publications
Hall, M. (2004). CD-mapping derived from ultrasonic TSI-GM profiles. Sundsvall: Mitthögskolan, FSCN
Open this publication in new window or tab >>CD-mapping derived from ultrasonic TSI-GM profiles
2004 (English)Report (Other academic)
Place, publisher, year, edition, pages
Sundsvall: Mitthögskolan, FSCN, 2004. p. 10
Series
Rapportserie FSCN, ISSN 1650-5387 ; 2004:26
Series
FSCN-rapport ; R-04-53
Identifiers
urn:nbn:se:miun:diva-9274 (URN)
Available from: 2009-07-09 Created: 2009-07-09 Last updated: 2009-12-01Bibliographically approved
Pavlenko, T., Hall, M., von Rosen, D. & Andrushchenko, Z. (2004). Towards the optimal feature selection in high-dimensional Bayesian network classifiers. In: LopezDiaz, M; Gil, MA; Grzegorzewski, P; Hryniewicz, O; Lawry, J (Ed.), SOFT METHODOLOGY AND RANDOM INFORMATION SYSTEMS. Paper presented at 2nd International Conference on Soft Methods in Probability and Statistics (SMPS 2004), Sep 02, 2004-Sep 04, 2004 (pp. 613-620). SPRINGER-VERLAG BERLIN
Open this publication in new window or tab >>Towards the optimal feature selection in high-dimensional Bayesian network classifiers
2004 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
SPRINGER-VERLAG BERLIN, 2004
Series
ADVANCES IN SOFT COMPUTING, ISSN 1615-3871
National Category
Mathematics
Identifiers
urn:nbn:se:miun:diva-13492 (URN)000224212800076 ()3-540-22264-2 (ISBN)
Conference
2nd International Conference on Soft Methods in Probability and Statistics (SMPS 2004), Sep 02, 2004-Sep 04, 2004
Available from: 2011-04-08 Created: 2011-04-08 Last updated: 2011-04-08Bibliographically approved
Pavlenko, T., Hall, M. & Rosen, D. v. (2004). Towards the optimal feature selection in high-dimensional bayesian network classifiers. Umeå: SLU, Centre of Biostochastics
Open this publication in new window or tab >>Towards the optimal feature selection in high-dimensional bayesian network classifiers
2004 (English)Report (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.

Place, publisher, year, edition, pages
Umeå: SLU, Centre of Biostochastics, 2004. p. 14
Series
Research report / Centre of Biostochastics,, ISSN 1651-8543 ; 2004 : 1
Keywords
Bayesian network, augmenting, separation strength, growing dimension
National Category
Mathematics
Identifiers
urn:nbn:se:miun:diva-5653 (URN)1677 (Local ID)1677 (Archive number)1677 (OAI)
Available from: 2008-09-30 Created: 2008-09-30 Last updated: 2011-04-12Bibliographically approved

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