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IWANN
2009
Springer

Feature Selection in Survival Least Squares Support Vector Machines with Maximal Variation Constraints

11 years 20 days ago
Feature Selection in Survival Least Squares Support Vector Machines with Maximal Variation Constraints
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 L1 regularization scheme. Key words: failure time data, feature selection, ls-svm
Vanya Van Belle, Kristiaan Pelckmans, Johan A. K.
Added 27 May 2010
Updated 27 May 2010
Type Conference
Year 2009
Where IWANN
Authors Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. Suykens, Sabine Van Huffel
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