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Enhancing decision-level fusion through cluster-based partitioning of feature set
Kaunas University of Technology, Kaunas, Litauen.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).ORCID iD: 0000-0003-2185-8973
Kaunas University of Technology, Kaunas, Litauen.
Kaunas University of Technology, Kaunas, Litauen.
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2014 (English)In: The MENDEL Soft Computing journal : International Conference on Soft Computing MENDEL, ISSN 1803-3814, 259-264 p.Article in journal (Refereed) Published
Abstract [en]

Feature set decomposition through cluster-based partitioning is the subject of this study. Approach is applied for the detection of mild laryngeal disorder from acoustic parameters of human voice using random forest (RF) as a base classier. Observations of sustained phonation (audio recordings of vowel /a/) had clinical diagnosis and severity level (from 0 to 3), but only healthy (severity 0) and mildly pathological (severity 1) cases were used. Diverse feature set (made of 26 variously sized subsets) was extracted from the voice signal. Feature-and decision-level fusions showed improvement over the best individual feature subset, but accuracy of fusion strategies did not differ signicantly. To boost accuracy of decision-level fusion, unsupervised decomposition for ensemble design was proposed. Decomposition was obtained by feature-space re-partitioning through clustering. Algorithms tested: a) basic k-Means; b) non-parametric MeanNN; c) adaptive anity propagation. Clustering by k-Means signicantly outperformed feature- and decision-level fusions.

Place, publisher, year, edition, pages
Brno, Czech Republic: Mendel University in Brno , 2014. 259-264 p.
Keyword [en]
random forest, ensemble of classiers, feature-space decomposition, clustering, k-Means, MeanNN, anity propagation, pathological voice
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hh:diva-26559DOI: 10.13140/2.1.2800.4481Scopus ID: 2-s2.0-84938053992OAI: oai:DiVA.org:hh-26559DiVA: diva2:748735
Conference
20th International Conference on Soft Computing MENDEL 2014, Brno, Czech Republic, June 25 - 27, 2014
Available from: 2014-09-22 Created: 2014-09-22 Last updated: 2017-03-15Bibliographically approved

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Verikas, Antanas
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CiteExportLink to record
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Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf