caa a22 4500 606160531 CHVBK 20210128100630.0 cr unu---uuuuu 210128e20150201xx s 000 0 eng 10.1007/s00521-014-1634-9 doi (NATIONALLICENCE)springer-10.1007/s00521-014-1634-9 Fusion trees for fast and accurate classification of hyperspectral data with ensembles of $$\gamma$$ γ -divergence-based RBF networks [Elektronische Daten] [Uwe Knauer, Andreas Backhaus, Udo Seiffert] Ensembles of RBF networks trained with $$\gamma$$ γ -divergence-based similarity measures can improve classification accuracy of hyperspectral imaging data significantly compared to any single RBF network as well as to RBF ensembles based on the Euclidian distance. So far, the drawback of using classifier ensembles is the need to compute the results of a typically large number of RBF networks prior to combination. In this paper, a modified approach to the fusion of classifier outputs is proposed which is based on decision trees. It is shown for several real-world datasets that a small subset of the RBF networks contributes to the decisions in the average case. Hence, for any decision, a conditional computation of required RBF network outputs yields a significant decrease in the computational costs. Additionally, a selection scheme for subsets of RBF classifiers based on their relevance in the fusion process is proposed. This alternative approach can be used, if analysis requires fixed settings, e.g., to meet time constraints. Springer-Verlag London, 2014 RBF nationallicence Fusion nationallicence Feature selection nationallicence Hyperspectral data nationallicence Functional data nationallicence γ -divergence nationallicence Knauer Uwe Fraunhofer IFF, Sandtorstr. 22, 39106, Magdeburg, Germany aut Backhaus Andreas Fraunhofer IFF, Sandtorstr. 22, 39106, Magdeburg, Germany aut Seiffert Udo Fraunhofer IFF, Sandtorstr. 22, 39106, Magdeburg, Germany aut Neural Computing and Applications Springer London 26/2(2015-02-01), 253-262 0941-0643 26:2<253 2015 26 521 https://doi.org/10.1007/s00521-014-1634-9 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00521-014-1634-9 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Knauer Uwe Fraunhofer IFF, Sandtorstr. 22, 39106, Magdeburg, Germany aut NATIONALLICENCE 700 1- Backhaus Andreas Fraunhofer IFF, Sandtorstr. 22, 39106, Magdeburg, Germany aut NATIONALLICENCE 700 1- Seiffert Udo Fraunhofer IFF, Sandtorstr. 22, 39106, Magdeburg, Germany aut NATIONALLICENCE 773 0- Neural Computing and Applications Springer London 26/2(2015-02-01), 253-262 0941-0643 26:2<253 2015 26 521