Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
A Unifeid Bias-Variance Decomposition and its Applications
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
The Journal of Machine Learning Research
An introduction to variable and feature selection
The Journal of Machine Learning Research
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Grafting: fast, incremental feature selection by gradient descent in function space
The Journal of Machine Learning Research
Learning to Decode Cognitive States from Brain Images
Machine Learning
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
The Entire Regularization Path for the Support Vector Machine
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
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
A stability index for feature selection
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Robust Feature Selection Using Ensemble Feature Selection Techniques
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Consensus group stable feature selection
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
On Feature Selection, Bias-Variance, and Bagging
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
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Stability of feature selection is an important but under-addressed issue in knowledge discovery from high-dimensional data. In this study, we present a theoretical framework about the relationship between the stability and the accuracy of feature selection based on a formal bias-variance decomposition of feature selection error. The framework also reveals the connection between stability and sample size and suggests a variance reduction approach for improving the stability of feature selection algorithms under small sample size. Following the theoretical framework, we propose an empirical variance reduction framework, margin-based instance weighting, which weights training instances according to their importance to feature evaluation. Our extensive experimental study first verifies the theoretical and empirical frameworks based on synthetic data sets and a popular feature selection algorithm SVM-RFE. Experiments based on real-world microarray data sets further verify that the empirical framework is effective at reducing the variance and improving the subset stability of two representative feature selection algorithms, SVM-RFE and ReliefF, while maintaining comparable predictive accuracy based on the selected features. The proposed instance weighting framework is also shown to be more effective and efficient than the ensemble framework at improving the subset stability of the feature selection algorithms under study. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2012 © 2012 Wiley Periodicals, Inc.