JZUS - Journal of Zhejiang University SCIENCE

Journal of Zhejiang University SCIENCE A 2005 Vol.6 No.5 P.387-392

http://doi.org/10.1631/jzus.2005.A0387

Clustering-based selective neural network ensemble

Author(s): FU Qiang, HU Shang-xu, ZHAO Sheng-ying
Affiliation(s): Laboratory of Intelligence Information Engineering, Zhejiang University, Hangzhou 310027, China; more
Corresponding email(s): fuqiang@zju.edu.cn
Key Words: Neural network, Ensemble, Clustering

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Abstract: An effective ensemble should consist of a set of networks that are both accurate and diverse. We propose a novel clustering-based selective algorithm for constructing neural network ensemble, where clustering technology is used to classify trained networks according to similarity and optimally select the most accurate individual network from each cluster to make up the ensemble. Empirical studies on regression of four typical datasets showed that this approach yields significantly smaller ensemble achieving better performance than other traditional ones such as Bagging and Boosting. The bias variance decomposition of the predictive error shows that the success of the proposed approach may lie in its properly tuning the bias/variance trade-off to reduce the prediction error (the sum of bias² and variance).

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