Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
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
Chi2: Feature Selection and Discretization of Numeric Attributes
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
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
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
Feature selection of intrusion detection data using a hybrid genetic algorithm/KNN approach
Design and application of hybrid intelligent systems
Cost-Guided Class Noise Handling for Effective Cost-Sensitive Learning
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
IEEE Transactions on Pattern Analysis and Machine Intelligence
Class noise vs. attribute noise: a quantitative study of their impacts
Artificial Intelligence Review
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
A review of feature selection techniques in bioinformatics
Bioinformatics
Consensus group stable feature selection
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge discovery from imbalanced and noisy data
Data & Knowledge Engineering
A Study on the Relationships of Classifier Performance Metrics
ICTAI '09 Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence
An Empirical Study on Wrapper-Based Feature Ranking
ICTAI '09 Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence
Wrapper-Based Feature Ranking for Software Engineering Metrics
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
Improving stability of feature selection methods
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Correlation-based detection of attribute outliers
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
A Comparative Study of Threshold-Based Feature Selection Techniques
GRC '10 Proceedings of the 2010 IEEE International Conference on Granular Computing
Comparative Analysis of DNA Microarray Data through the Use of Feature Selection Techniques
ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
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One very common criterion used to evaluate feature selection methods is the performance of a chosen classifier trained with the selected features. Another important evaluation criterion that has, until recently, been neglected is the stability of these feature selection methods. While other studies have shown interest in measuring the degree of agreement between the outputs of a technique trained on randomly selected subsets from the same input data, this study presents the importance of evaluating stability in the presence of noise. Experiments are conducted with 17 filters (six standard filter-based ranking techniques and 11 threshold-based feature selection techniques) on nine different real-world datasets. This paper identifies the techniques that are inherently more sensitive to class noise and demonstrates how certain characteristics (sample size and class imbalance) of the data can affect the stability performance of some feature selection methods.