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This paper presents an improved elastic net to identify relevant genes for cancer classification. By introducing the data-driven weight coefficients, the improved elastic net can adaptively select genes in groups and reduce the shrinkage bias for the coefficients of significant genes. Moreover, the irrelevant observations on the augmented dataset are removed and the computational complexity is largely...
This paper proposes a new multiclass support vector machine (SVM) for simultaneous gene selection and microarray classification. Combining the huberized hinge loss function and the elastic net penalty, the proposed SVM can perform automatic gene selection and encourages a grouping effect. The coefficient paths of the proposed SVM are shown to be piecewise linear with respect to the single regularization...
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