Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
An introduction to variable and feature selection
The Journal of Machine Learning Research
Variable selection using svm based criteria
The Journal of Machine Learning Research
Predicting survival from microarray data—a comparative study
Bioinformatics
Componentwise support vector machines for structure detection
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
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This work proposes the use of maximal variation analysis for feature selection within least squares support vector machines for survival analysis. Instead of selecting a subset of variables with forward or backward feature selection procedures, we modify the loss function in such a way that the maximal variation for each covariate is minimized, resulting in models which have sparse dependence on the features. Experiments on artificial data illustrate the ability of the maximal variation method to recover relevant variables from the given ones. A real life study concentrates on a breast cancer dataset containing clinical variables. The results indicate a better performance for the proposed method compared to Cox regression with an L 1 regularization scheme.