Abstract
Feature ordering is important in Incremental Attribute Learning where features are gradually trained in one or more size. Apart from time-consuming contribution-based feature ordering methods, feature ordering also can be derived by filter criteria. In this paper, a novel criterion based on a new metric called Discriminability is presented to give ranks for feature ordering. Final results show that the new metric not only is applicable for IAL, but also exhibits better performance in lower error rates.
Original language | English |
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Title of host publication | Foundations of Intelligent Systems |
Subtitle of host publication | Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE2011) |
Editors | Yinglin Wang, Tianrui Li |
Pages | 275-280 |
Number of pages | 6 |
DOIs | |
Publication status | Published - 2011 |
Publication series
Name | Advances in Intelligent and Soft Computing |
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Volume | 122 |
ISSN (Print) | 1867-5662 |
Keywords
- feature ordering
- incremental attribute learning
- neural networks
Cite this
Wang, T., Guan, S. U., & Liu, F. (2011). Feature discriminability for pattern classification based on neural incremental attribute learning. In Y. Wang, & T. Li (Eds.), Foundations of Intelligent Systems: Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE2011) (pp. 275-280). (Advances in Intelligent and Soft Computing; Vol. 122). https://doi.org/10.1007/978-3-642-25664-6_32