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Selecting features of linear-chain conditional random fields via greedy stage-wise algorithms
- Source :
-
Pattern Recognition Letters . Jan2010, Vol. 31 Issue 2, p151-162. 12p. - Publication Year :
- 2010
-
Abstract
- Abstract: This paper presents two embedded feature selection algorithms for linear-chain CRFs named GFSA_LCRF and PGFSA_LCRF. GFSA_LCRF iteratively selects a feature incorporating which into the CRF will improve the conditional log-likelihood of the CRF most at one time. For time efficiency, only the weight of the new feature is optimized to maximize the log-likelihood instead of all weights of features in the CRF. The process is iterated until incorporating new features into the CRF can not improve the log-likelihood of the CRF noticeably. PGFSA_LCRF adopts pseudo-likelihood as evaluation criterion to iteratively select features to improve the speed of GFSA_LCRF. Furthermore, it scans all candidate features and forms a small feature set containing some promising features at certain iterations. Then, the small feature set will be used by subsequent iterations to further improve the speed. Experiments on two real-world problems show that CRFs with significantly fewer features selected by our algorithms achieve competitive performance while obtaining significantly shorter testing time. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 01678655
- Volume :
- 31
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition Letters
- Publication Type :
- Academic Journal
- Accession number :
- 45413324
- Full Text :
- https://doi.org/10.1016/j.patrec.2009.09.025