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首页> 外文期刊>Journal of Intelligent Learning Systems and Applications >Fast Variable Selection by Block Addition and Block Deletion
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Fast Variable Selection by Block Addition and Block Deletion

机译：通过块添加和块删除快速选择变量

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摘要

We propose the threshold updating method for terminating variable selection and two variable selection methods. In the threshold updating method, we update the threshold value when the approximation error smaller than the current threshold value is obtained. The first variable selection method is the combination of forward selection by block addi-tion and backward selection by block deletion. In this method, starting from the empty set of the input variables, we add several input variables at a time until the approximation error is below the threshold value. Then we search deletable variables by block deletion. The second method is the combination of the first method and variable selection by Linear Programming Support Vector Regressors (LPSVRs). By training an LPSVR with linear kernels, we evaluate the weights of the decision function and delete the input variables whose associated absolute weights are zero. Then we carry out block addition and block deletion. By computer experiments using benchmark data sets, we show that the proposed methods can perform faster variable selection than the method only using block deletion, and that by the threshold updating method, the approximation error is lower than that by the fixed threshold method. We also compare our method with an imbedded method, which determines the optimal variables during training, and show that our method gives comparable or better variable selection performance.
机译：我们提出了终止变量选择的阈值更新方法和两种变量选择方法。在阈值更新方法中，当获得的近似误差小于当前阈值时，我们更新阈值。第一种变量选择方法是通过块添加进行前向选择和通过块删除进行后向选择的组合。在此方法中，从空的一组输入变量开始，我们一次添加多个输入变量，直到逼近误差低于阈值为止。然后我们通过块删除搜索可删除的变量。第二种方法是第一种方法与通过线性编程支持向量回归（LPSVR）选择变量的组合。通过使用线性核训练LPSVR，我们评估决策函数的权重，并删除关联的绝对权重为零的输入变量。然后我们进行块添加和块删除。通过使用基准数据集的计算机实验，我们表明，与仅使用块删除的方法相比，所提出的方法可以执行更快的变量选择，并且通过阈值更新方法，逼近误差低于固定阈值方法。我们还将我们的方法与嵌入式方法进行了比较，后者确定了训练期间的最佳变量，并表明我们的方法具有可比或更好的变量选择性能。

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