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Enhancing decision-level fusion through cluster-based partitioning of feature set
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- Vaiciukynas, Evaldas (author)
- Kaunas University of Technology, Kaunas, Litauen
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- Verikas, Antanas, 1951- (author)
- Högskolan i Halmstad,Halmstad Embedded and Intelligent Systems Research (EIS)
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- Bacauskiene, Marija (author)
- Kaunas University of Technology, Kaunas, Litauen
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- Brno, Czech Republic : Mendel University in Brno, 2014
- 2014
- English.
- In: The MENDEL Soft Computing journal. - Brno, Czech Republic : Mendel University in Brno. - 1803-3814. ; , s. 259-264
- Journal article (peer-reviewed)
Abstract
Subject headings
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- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Annan elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Other Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Keyword
- random forest
- ensemble of classiers
- feature-space decomposition
- clustering
- k-Means
- MeanNN
- anity propagation
- pathological voice
Publication and Content Type
- ref (subject category)
- art (subject category)
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