应用突函数差异算法进行癌症分类的基因选择(英文)

研究了有关癌症分类的基因选择问题。开发了集成的基于平滑剪切绝对偏差罚分的SVM—特征选择方法,直接最小化分类器的性能。为解决优化问题,应用了突函数差异算法(difference of convex functionsal-gorithms,DCA)这一进行非突连续优化的通用框架,致使连续线性规划算法有限收敛。真实数据集上的先验实验表明算法达到了预想目标:在压缩大量属性的同时,保持了较小分类差错。

Gene Selection for Cancer Classification Using DCA

The problem of gene selection for cancer classification is considered. A combined SVM-feature selection approach based on the smoothly clipped absolute deviation （SCAD） penalty is developed， minimizing directly the classifier performance. To solve the optimization problems， apply the DCA （difference of convex functions algori-thms） which is a general framework for nonconvex continuous optimization. This leads to a successive linear programming algorithm with finite convergence. Preliminary computational experiments on different real data demonstrate that this method accomplishes the desired goal： Suppression of a large number of features with a small error of classification.