Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Inference for the Generalization Error
Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Use of the zero norm with linear models and kernel methods
The Journal of Machine Learning Research
Regularized multi--task learning
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to learn with the informative vector machine
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Logistic regression with an auxiliary data source
ICML '05 Proceedings of the 22nd international conference on Machine learning
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
Proceedings of the 25th international conference on Machine learning
Transferred Dimensionality Reduction
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Partially supervised feature selection with regularized linear models
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Efficient case based feature construction
ECML'05 Proceedings of the 16th European conference on Machine Learning
Review Article: Stable feature selection for biomarker discovery
Computational Biology and Chemistry
Expectation propagation for Bayesian multi-task feature selection
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
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)
Estimating mutual information for feature selection in the presence of label noise
Computational Statistics & Data Analysis
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This paper presents a novel feature selection method for classification of high dimensional data, such as those produced by microarrays. It includes a partial supervision to smoothly favor the selection of some dimensions (genes) on a new dataset to be classified. The dimensions to be favored are previously selected from similar datasets in large microarray databases, hence performing inductive transfer learning at the feature level. This technique relies on a feature selection method embedded within a regularized linear model estimation. A practical approximation of this technique reduces to linear SVM learning with iterative input rescaling. The scaling factors depend on the selected dimensions from the related datasets. The final selection may depart from those whenever necessary to optimize the classification objective. Experiments on several microarray datasets show that the proposed method both improves the selected gene lists stability, with respect to sampling variation, as well as the classification performances.