R中的svm特性选择示例

Question

我尝试使用R包在SVM中应用特征选择（例如，递归特征选择）。我已经安装了支持在LibSVM中进行功能选择的Weka，但是我还没有找到任何SVM或类似语法的例子。一个简短的例子会有很大的帮助。

Answer 1

caret包中的函数rfe为各种算法执行递归特征选择。下面是来自caretdocumentation一个例子：

library(caret) 
data(BloodBrain, package="caret") 
x <- scale(bbbDescr[,-nearZeroVar(bbbDescr)]) 
x <- x[, -findCorrelation(cor(x), .8)] 
x <- as.data.frame(x) 
svmProfile <- rfe(x, logBBB, 
        sizes = c(2, 5, 10, 20), 
        rfeControl = rfeControl(functions = caretFuncs, 
              number = 200), 
        ## pass options to train() 
        method = "svmRadial") 

# Here's what your results look like (this can take some time) 
> svmProfile 

Recursive feature selection 

Outer resampling method: Bootstrap (200 reps) 

Resampling performance over subset size: 

    Variables RMSE Rsquared RMSESD RsquaredSD Selected 
2 0.6106 0.4013 0.05581 0.08162   
5 0.5689 0.4777 0.05305 0.07665   
10 0.5510 0.5086 0.05253 0.07222   
20 0.5203 0.5628 0.04892 0.06721   
71 0.5202 0.5630 0.04911 0.06703  * 

    The top 5 variables (out of 71): 
    fpsa3, tcsa, prx, tcpa, most_positive_charge