Liao et al. (Neurocomputing 128:81–87, 2014) proposed a meta-learning approach to extreme learning machine (Meta-ELM), which can obtain good generalization performance by training multiple ELMs. However, one of its open problems is overfitting when minimizing training error. In this paper, we propose an improved meta-learning model of ELM (improved Meta-ELM) to handle the problem. The improved Meta-ELM architecture is composed of some base ELMs which are error feedback incremental extreme learning machine (EFI-ELM) and the top ELM. The improved Meta-ELM includes two stages. First, each base ELM with EFI-ELM is trained on a subset of training data. Then, the top ELM learns with the base ELMs as hidden nodes. Simulation results on some artificial and benchmark datasets show that the proposed improved Meta-ELM model is more feasible and effective than Meta-ELM.
Meta-ELM Overfitting EFI-ELM Heterogeneous
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Conflict of interest
The authors (Weidong Zou, Fenxi Yao, Baihai Zhang, Zixiao Guan) of paper (Title: Improved Meta-ELM with error feedback incremental ELM as hidden nodes, NCAA-D-16-00405-R2) declare that there is no conflict of interests.
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