Enhancing decision-level fusion through cluster-based partitioning of feature set
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
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, 2014. 259-264 p.
random forest, ensemble of classiers, feature-space decomposition, clustering, k-Means, MeanNN, anity propagation, pathological voice
Other Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:hh:diva-26559DOI: 10.13140/2.1.2800.4481OAI: oai:DiVA.org:hh-26559DiVA: diva2:748735
20th International Conference on Soft Computing MENDEL 2014, Brno, Czech Republic, June 25 - 27, 2014