Publications:Enhancing decision-level fusion through cluster-based partitioning of feature set
From ISLAB/CAISR
| Title | Enhancing decision-level fusion through cluster-based partitioning of feature set |
|---|---|
| Author | Evaldas Vaiciukynas and Antanas Verikas and Marija Bacauskiene and Adas Gelzinis and Zvi Kons |
| Year | 2014 |
| PublicationType | Journal Paper |
| Journal | The MENDEL Soft Computing journal : International Conference on Soft Computing MENDEL |
| HostPublication | |
| Conference | 20th International Conference on Soft Computing MENDEL 2014, Brno, Czech Republic, June 25 - 27, 2014 |
| DOI | http://dx.doi.org/10.13140/2.1.2800.4481 |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:748735 |
| 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. |
