Compartir
Titre
WeVoS-ViSOM: An ensemble summarization algorithm for enhanced data visualization
Autor(es)
Sujet
Computer Science
Fecha de publicación
2012
Éditeur
Elsevier BV
Citación
Neurocomputing. Volumen 75 (1), pp. 171-184. Elsevier BV.
Resumen
This study presents a novel version of the Visualization Induced Self-Organizing Map based on the application of a new fusion algorithm for summarizing the results of an ensemble of topology-preserving mapping models. The algorithm is referred to as Weighted Voting Superposition (WeVoS). Its main feature is the preservation of the topology of the map, in order to obtain the most accurate possible visualization of the data sets under study. To do so, a weighted voting process between the units of the maps in the ensemble takes place, in order to determine the characteristics of the units of the resulting map. Several different quality measures are applied to this novel neural architecture known as WeVoS-ViSOM and the results are analyzed, so as to present a thorough study of its capabilities. To complete the study, it has also been compared with the well-know SOM and its fusion version, with the WeVoS-SOM and with two other previously devised fusion Fusion by Euclidean Distance and Fusion by Voronoi Polygon Similarity—based on the analysis of the same quality measures in order to present a complete analysis of its capabilities. All three summarization methods were applied to three widely used data sets from the UCI Repository. A rigorous performance analysis clearly demonstrates that the novel fusion algorithm outperforms the other single and summarization methods in terms of data sets visualization.
URI
ISSN
0925-2312 (Print)
Aparece en las colecciones
- BISITE. Artículos [289]
Fichier(s) constituant ce document
Tamaño:
3.151Mo
Formato:
Adobe PDF