An introduction to computational learning theory
An introduction to computational learning theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Feature Selection: Learning with Exponentially Many Irrelevant Features as Training Examples
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Minimum Redundancy Feature Selection from Microarray Gene Expression Data
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Redundant feature elimination for multi-class problems
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
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Stability of feature selection algorithms: a study on high-dimensional spaces
Knowledge and Information Systems
Minimum reference set based feature selection for small sample classifications
Proceedings of the 24th international conference on Machine learning
Supervised feature selection via dependence estimation
Proceedings of the 24th international conference on Machine learning
Stable feature selection via dense feature groups
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Review Article: Stable feature selection for biomarker discovery
Computational Biology and Chemistry
Margin based sample weighting for stable feature selection
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Network-based sparse Bayesian classification
Pattern Recognition
Robust Feature Selection for Microarray Data Based on Multicriterion Fusion
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Feature selection stability assessment based on the Jensen-Shannon divergence
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
A novel stability based feature selection framework for k-means clustering
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Stable Gene Selection from Microarray Data via Sample Weighting
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Energy-based feature selection and its ensemble version
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Model mining for robust feature selection
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Measuring stability of feature ranking techniques: a noise-based approach
International Journal of Business Intelligence and Data Mining
A variance reduction framework for stable feature selection
Statistical Analysis and Data Mining
Sparse high-dimensional fractional-norm support vector machine via DC programming
Computational Statistics & Data Analysis
Stable Feature Selection with Minimal Independent Dominating Sets
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
Feature selection for k-means clustering stability: theoretical analysis and an algorithm
Data Mining and Knowledge Discovery
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Stability is an important yet under-addressed issue in feature selection from high-dimensional and small sample data. In this paper, we show that stability of feature selection has a strong dependency on sample size. We propose a novel framework for stable feature selection which first identifies consensus feature groups from subsampling of training samples, and then performs feature selection by treating each consensus feature group as a single entity. Experiments on both synthetic and real-world data sets show that an algorithm developed under this framework is effective at alleviating the problem of small sample size and leads to more stable feature selection results and comparable or better generalization performance than state-of-the-art feature selection algorithms. Synthetic data sets and algorithm source code are available at http://www.cs.binghamton.edu/~lyu/KDD09/.