A maximum entropy approach to natural language processing
Computational Linguistics
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Machine Learning for Sequential Data: A Review
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Information Extraction with HMM Structures Learned by Stochastic Optimization
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Training conditional random fields via gradient tree boosting
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Shallow parsing with conditional random fields
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
2D Conditional Random Fields for Web information extraction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Accelerated training of conditional random fields with stochastic gradient methods
ICML '06 Proceedings of the 23rd international conference on Machine learning
The Journal of Machine Learning Research
Biomedical named entity recognition using conditional random fields and rich feature sets
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Efficiently inducing features of conditional random fields
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Segmenting brain tumors with conditional random fields and support vector machines
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
| Hi-index | 0.10 |
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.