Abstract
Extreme learning machine (ELM) was proposed as a new algorithm for training single-hidden layer feed-forward neural networks (SLFNs). One of the issues in EML is how to determine the architecture of SLFNs. Based on sensitivity of hidden nodes, an approach of architecture selection of ELM networks by applying a pruned method was proposed in this paper. The proposed pruning method utilizes sensitivity to measure the significance of hidden nodes. Beginning from an initial large number of hidden nodes, the insignificant nodes with lower sensitivity are then pruned. Experimental results on ten UCI data sets show that the proposed approach can obtain compact network architecture that generate comparable prediction accuracy on unseen samples.
| Original language | English |
|---|---|
| Pages (from-to) | 471-489 |
| Number of pages | 19 |
| Journal | Neural Processing Letters |
| Volume | 44 |
| Issue number | 2 |
| Early online date | 18 Sept 2015 |
| DOIs | |
| Publication status | Published - Oct 2016 |
| Externally published | Yes |
Bibliographical note
This research is supported by the national natural science foundation of China (61170040, 71371063), by the natural science foundation of Hebei Province (F2013201110, F2013201220), by the Key Scientific Research Foundation of Education Department of Hebei Province (ZD20131028), and by the Opening Fund of Zhejiang Provincial Top Key Discipline of Computer Science and Technology at Zhejiang Normal University, China.Keywords
- Architecture selection
- Extreme learning machine (ELM)
- Feed-forward networks
- Pruning
- Sensitivity