Ensemble of Single-Layered Complex-Valued Neural Networks for Classification Tasks

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Author: AMIN, Md. Faijulほか

Publisher: ELSEVIER SCIENCE B.V.

Publication date: 2009-06

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Note (General)：: 出版タイプ： AOThis paper presents ensemble approaches in single-layered complex-valued neural network (CVNN) to solve real-valued classification problems. ...

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Material Type: その他
Title: Ensemble of Single-Layered Complex-Valued Neural Networks for Classification Tasks
Author/Editor: AMIN, Md. Faijul
ISLAM, Md. Monirul
MURASE, Kazuyuki
Author Heading: AMIN, Md. Faijul
ISLAM, Md. Monirul
MURASE, Kazuyuki
Publication, Distribution, etc.: ELSEVIER SCIENCE B.V.
Publication Date: 2009-06
Publication Date (W3CDTF): 2009
Periodical title: Neurocomputing
No. or year of volume/issue: 72 10-12
Volume: 72
Issue: 10-12
Pages: 2227-2234
Subject Heading: activation function
classification
complex-valued neural network
ensemble
Target Audience: 一般
Note (General): 出版タイプ： AO
This paper presents ensemble approaches in single-layered complex-valued neural network (CVNN) to solve real-valued classification problems. Each component CVNN of an ensemble uses a recently proposed activation function for its complex-valued neurons (CVNs). A gradient-descent based learning algorithm was used to train the component CVNNs. We applied two ensemble methods, negative correlation learning and bagging, to create the ensembles. Experimental results on a number of real-world benchmark problems showed a substantial performance improvement of the ensembles over the individual single-layered CVNN classifiers. Furthermore, the generalization performances were nearly equivalent to those obtained by the ensembles of real-valued multilayer neural networks.
Related Material (DOI): 10.1016/j.neucom.2008.12.028
https://doi.org/10.1016/j.neucom.2008.12.028
Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）
https://irdb.nii.ac.jp
Original Data Provider (Database): 福井大学 : 福井大学学術機関リポジトリ