JACIII Vol.15 No.6 pp. 652-661
doi: 10.20965/jaciii.2011.p0652


Hybrid Ensemble Construction with Selected Neural Networks

M. A. H. Akhand*, Pintu Chandra Shill**,
and Kazuyuki Murase**

*Dept. of Computer Science and Engineering, Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh

**Dept. of Human and Artificial Intelligence Systems, Graduate School of Engineering, University of Fukui

December 24, 2010
May 6, 2011
August 20, 2011
diversity, generalization, neural network ensemble, network selection
A Neural Network Ensemble (NNE) is convenient for improving classification task performance. Among the remarkable number of methods based on different techniques for constructing NNEs, Negative Correlation Learning (NCL), bagging, and boosting are the most popular. None of them, however, could show better performance for all problems. To improve performance combining the complementary strengths of the individual methods, we propose two different ways to construct hybrid ensembles combining NCL with bagging and boosting. One produces a pool of predefined numbers of networks using standard NCL and bagging (or boosting) and then uses a genetic algorithm to select an optimal network subset for an NNE from the pool. Results of experiments confirmed that our proposals show consistently better performance with concise ensembles than conventional methods when tested using a suite of 25 benchmark problems.
Cite this article as:
M. Akhand, P. Shill, and K. Murase, “Hybrid Ensemble Construction with Selected Neural Networks,” J. Adv. Comput. Intell. Intell. Inform., Vol.15 No.6, pp. 652-661, 2011.
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