C4.5: programs for machine learning
C4.5: programs for machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
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
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
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
Bioinformatics
Random Forests for multiclass classification: Random MultiNomial Logit
Expert Systems with Applications: An International Journal
A review of feature selection techniques in bioinformatics
Bioinformatics
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
Journal of Artificial Evolution and Applications - Special issue on artificial evolution methods in the biological and biomedical sciences
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
A Multiple-Filter-Multiple-Wrapper Approach to Gene Selection and Microarray Data Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Efficient learning and feature selection in high-dimensional regression
Neural Computation
Informative feature selection for object recognition via Sparse PCA
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Recent research efforts attempt to combine multiple feature selection techniques instead of using a single one. However, this combination is often made on an ''ad hoc'' basis, depending on the specific problem at hand, without considering the degree of diversity/similarity of the involved methods. Moreover, though it is recognized that different techniques may return quite dissimilar outputs, especially in high dimensional/small sample size domains, few direct comparisons exist that quantify these differences and their implications on classification performance. This paper aims to provide a contribution in this direction by proposing a general methodology for assessing the similarity between the outputs of different feature selection methods in high dimensional classification problems. Using as benchmark the genomics domain, an empirical study has been conducted to compare some of the most popular feature selection methods, and useful insight has been obtained about their pattern of agreement.