R中的svm功能选择示例

问题描述

我正在尝试使用R包在SVM中应用功能选择(例如，递归功能选择).我已经安装了Weka，它支持LibSVM中的功能选择，但是我还没有找到SVM语法或类似内容的任何示例.一个简短的例子会很有帮助.

I'm trying to apply feature selection (e.g. recursive feature selection) in SVM, using the R package. I've installed Weka which supports feature selection in LibSVM but I haven't found any example for the syntax of SVM or anything similar. A short example would be of a great help.

推荐答案

caret包中的函数rfe为各种算法执行递归特征选择.以下是caret 文档中的示例:

The function rfe in the caret package performs recursive feature selection for various algorithms. Here's an example from the caret documentation:

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

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