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ELITE: Ensemble of Optimal Input-Pruned Neural Networks Using TRUST-TECH

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2 Author(s)
Bin Wang ; Sch. of Electr. & Comput. Eng., Cornell Univ., Ithaca, NY, USA ; Hsiao-Dong Chiang

The ensemble of optimal input-pruned neural networks using TRUST-TECH (ELITE) method for constructing high-quality ensemble through an optimal linear combination of accurate and diverse neural networks is developed. The optimization problems in the proposed methodology are solved by a global optimization a global optimization method called TRansformation Under Stability-reTraining Equilibrium Characterization (TRUST-TECH), whose main features include its capability in identifying multiple local optimal solutions in a deterministic, systematic, and tier-by-tier manner. ELITE creates a diverse population via a feature selection procedure of different local optimal neural networks obtained using tier-1 TRUST-TECH search. In addition, the capability of each input-pruned network is fully exploited through a TRUST-TECH-based optimal training. Finally, finding the optimal linear combination weights for an ensemble is modeled as a nonlinear programming problem and solved using TRUST-TECH and the interior point method, where the issue of non-convexity can be effectively handled. Extensive numerical experiments have been carried out for pattern classification on the synthetic and benchmark datasets. Numerical results show that ELITE consistently outperforms existing methods on the benchmark datasets. The results show that ELITE can be very promising for constructing high-quality neural network ensembles.

Published in:

Neural Networks, IEEE Transactions on  (Volume:22 ,  Issue: 1 )