Sökning: onr:"swepub:oai:DiVA.org:hh-26559" >
Enhancing decision-...
Enhancing decision-level fusion through cluster-based partitioning of feature set
-
- Vaiciukynas, Evaldas (författare)
- Kaunas University of Technology, Kaunas, Litauen
-
- Verikas, Antanas, 1951- (författare)
- Högskolan i Halmstad,Halmstad Embedded and Intelligent Systems Research (EIS)
-
- Bacauskiene, Marija (författare)
- Kaunas University of Technology, Kaunas, Litauen
-
visa fler...
-
- Gelzinis, Adas (författare)
- Kaunas University of Technology, Kaunas, Litauen
-
- Kons, Zvi (författare)
- IBM, Haifa, Israel
-
visa färre...
-
(creator_code:org_t)
- Brno, Czech Republic : Mendel University in Brno, 2014
- 2014
- Engelska.
-
Ingår i: The MENDEL Soft Computing journal. - Brno, Czech Republic : Mendel University in Brno. - 1803-3814. ; , s. 259-264
- Relaterad länk:
-
https://doi.org/10.1...
-
visa fler...
-
https://hh.diva-port... (primary) (Raw object)
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- 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)
Nyckelord
- random forest
- ensemble of classiers
- feature-space decomposition
- clustering
- k-Means
- MeanNN
- anity propagation
- pathological voice
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas