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摘要: 提出了一种能同时进行基因选择和微阵列分类的新型多类支持向量机. 通过结合huberized hinge 损失函数与弹性网络惩罚, 所提支持向量机能自动地进行基因选择并激励一种群体效应. 所提支持向量机的系数路关于单正则化参数是分段线性的, 并基于此发展了解路算法, 减少了计算的复杂性. 白血病数据集上的实验验证了所提方法的有效性.Abstract: 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 parameter, based on which the solution path algorithm is developed with low computational complexity. Experiments performed on the leukemia data set are provided to verify the obtained results.
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