Neural networks and the bias/variance dilemma
Neural Computation
Technical Note: Bias and the Quantification of Stability
Machine Learning - Special issue on bias evaluation and selection
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
A Unifeid Bias-Variance Decomposition and its Applications
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Unified Bias-Variance Decomposition for Zero-One and Squared Loss
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
Stable feature selection via dense feature groups
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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
Evaluating the Stability of Feature Selectors That Optimize Feature Subset Cardinality
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Consensus group stable feature selection
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Answering linear optimization queries with an approximate stream index
Knowledge and Information Systems
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Incremental Bayesian Network Learning for Scalable Feature Selection
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Feature Weighting Using Margin and Radius Based Error Bound Optimization in SVMs
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Margin and Radius Based Multiple Kernel Learning
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Stable and Accurate Feature Selection
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Stability analysis on rough set based feature evaluation
RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Review Article: Stable feature selection for biomarker discovery
Computational Biology and Chemistry
Knowledge and Information Systems
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
Ensemble logistic regression for feature selection
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
Stable Gene Selection from Microarray Data via Sample Weighting
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A new framework for dissimilarity and similarity learning
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
The Journal of Machine Learning Research
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
Feature extraction in protein sequences classification: a new stability measure
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
A variance reduction framework for stable feature selection
Statistical Analysis and Data Mining
Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data
Information Sciences: an International Journal
Learning neighborhoods for metric learning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
International Journal of Data Mining and Bioinformatics
Stable Feature Selection with Minimal Independent Dominating Sets
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
A new measure for gene expression biclustering based on non-parametric correlation
Computer Methods and Programs in Biomedicine
Analysis of feature selection stability on high dimension and small sample data
Computational Statistics & Data Analysis
A survey on feature selection methods
Computers and Electrical Engineering
Feature selection for k-means clustering stability: theoretical analysis and an algorithm
Data Mining and Knowledge Discovery
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With the proliferation of extremely high-dimensional data, feature selection algorithms have become indispensable components of the learning process. Strangely, despite extensive work on the stability of learning algorithms, the stability of feature selection algorithms has been relatively neglected. This study is an attempt to fill that gap by quantifying the sensitivity of feature selection algorithms to variations in the training set. We assess the stability of feature selection algorithms based on the stability of the feature preferences that they express in the form of weights-scores, ranks, or a selected feature subset. We examine a number of measures to quantify the stability of feature preferences and propose an empirical way to estimate them. We perform a series of experiments with several feature selection algorithms on a set of proteomics datasets. The experiments allow us to explore the merits of each stability measure and create stability profiles of the feature selection algorithms. Finally, we show how stability profiles can support the choice of a feature selection algorithm.