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1.	Vaiciukynas, Evaldas, et al. (författare) Enhancing decision-level fusion through cluster-based partitioning of feature set 2014 Ingår i: The MENDEL Soft Computing journal. - Brno, Czech Republic : Mendel University in Brno. - 1803-3814. ; , s. 259-264 Tidskriftsartikel (refereegranskat)abstract 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.

Vaiciukynas, Evaldas, et al. (författare)
Enhancing decision-level fusion through cluster-based partitioning of feature set
2014
Ingår i: The MENDEL Soft Computing journal. - Brno, Czech Republic : Mendel University in Brno. - 1803-3814. ; , s. 259-264
Tidskriftsartikel (refereegranskat)abstract
- 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.

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