In this paper, we propose two stream-based active learning algorithms for single-hidden layer feed-forward neural networks (SLFNs) trained by extreme learning machine (ELM). Uncertainty and inconsistency are adopted as two sample selection criteria. Uncertainty reflects the nondeterminacy of a sample among different decision classes, which is calculated by information entropy or Gini-index. Inconsistency reflects the disagreement of the sample between its conditional features and decision labels, which is calculated by the lower approximations in fuzzy rough sets. Experimental results demonstrate that inconsistency-based strategy is more effective than uncertainty based strategy for SLFNs under stream-based environment.
|Title of host publication||Proceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015|
|Publication status||Published - 12 Jan 2016|
Bibliographical noteNational Natural Science Foundation of China under Grant 61402460
- Active Learning
- Extreme Learning Machine