用于癌症分类与基因选择的一种改进的弹性网络

提出了一种用于辨识癌症分类的重要基因的改进弹性网络. 通过引入数据驱动权重系数, 改进的弹性网络能自适应地成群选择基因并减少重要基因对应系数的收缩偏好. 此外, 不相关观测被从增广数据集中消除从而大大减少了计算复杂性. 在急性白血病数据集上的实验结果验证了所提方法的有效性.

An Improved Elastic Net for Cancer Classification and Gene Selection

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 reduced. Experiment results on the acute leukaemia data are provided to verify the proposed method.