A Constructive Algorithm for Training Heterogeneous Neural Network Ensemble

Fu, Xianghua; Wang, Zhiqiang; Feng, Boqin

doi:10.1007/11795131_57

Xianghua Fu²²,
Zhiqiang Wang²² &
Boqin Feng²³

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4062))

Included in the following conference series:

International Conference on Rough Sets and Knowledge Technology

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Abstract

This paper presents a new algorithm to construct a neural network ensemble (NNE) based on heterogeneous component neural networks with negative correlation learning. The constructive algorithm consists of two parts: a sub-algorithm to construct best heterogeneous component neural networks with negative correlation learning dynamically (CBHNN), and a sub-algorithm to construct heterogeneous NNE with trained heterogeneous neural networks incrementally (CHNNE). The experiment results showe that HNNE is better than the traditional homological NNE method.

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Article 27 January 2017

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Authors and Affiliations

College of Information Engineering, Shenzhen University, Shenzhen, 518060, China
Xianghua Fu & Zhiqiang Wang
School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, China
Boqin Feng

Authors

Xianghua Fu
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Zhiqiang Wang
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Boqin Feng
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Editors and Affiliations

College of Computer Science and Technology, Chongqing, University of Posts and Telecommunication, 400065, Chongqing, P.R. China
Guo-Ying Wang
Department of Electrical and Computer Engineering, University of Manitoba, R3T 5V6, Winnipeg, Manitoba, Canada
James F. Peters
Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland
Andrzej Skowron
Department of Computer Science, University of Regina, Regina,, S4S 0A2, Saskatchewan, Canada
Yiyu Yao

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Fu, X., Wang, Z., Feng, B. (2006). A Constructive Algorithm for Training Heterogeneous Neural Network Ensemble. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_57

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DOI: https://doi.org/10.1007/11795131_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36297-5
Online ISBN: 978-3-540-36299-9
eBook Packages: Computer ScienceComputer Science (R0)

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