Question

我使用R代码从此源http://www.uccor.edu.ar/paginas/seminarios/Software/SVM_RFE_R_implementation.pdf实现SVM-RFE算法，但我进行了一些小修改，以便r代码使用gnum库。代码如下：

svmrfeFeatureRanking = function(x,y){
  n = ncol(x)

  survivingFeaturesIndexes = seq(1:n)
  featureRankedList = vector(length=n)
  rankedFeatureIndex = n

  while(length(survivingFeaturesIndexes)>0){
    #train the support vector machine
    svmModel = SVM(x[, survivingFeaturesIndexes], y, C = 10, cache_size=500,kernel="linear" )



    #compute ranking criteria
    rankingCriteria = svmModel$w * svmModel$w

    #rank the features
    ranking = sort(rankingCriteria, index.return = TRUE)$ix

    #update feature ranked list
    featureRankedList[rankedFeatureIndex] = survivingFeaturesIndexes[ranking[1]]
    rankedFeatureIndex = rankedFeatureIndex - 1

    #eliminate the feature with smallest ranking criterion
    (survivingFeaturesIndexes = survivingFeaturesIndexes[-ranking[1]])

  }

  return (featureRankedList)
}

该功能获得matrix input x { - {}} factor input y。我将函数用于某些数据，并在最后一次迭代中收到以下错误消息：

 Error in if (nrow(x) != length(y)) { : argument is of length zero

调试代码，我得到了这个：

3 SVM.default(x[, survivingFeaturesIndexes], y, C = 10, cache_size = 500, 
    kernel = "linear") 
2 SVM(x[, survivingFeaturesIndexes], y, C = 10, cache_size = 500, 
    kernel = "linear") 
1 svmrfeFeatureRanking(sdatx, ym)

那么，该函数的错误是什么？

Answer 1

当只剩下一个要素时，看起来您的矩阵会转换为列表。试试这个：

svmModel = SVM(as.matrix(x[, survivingFeaturesIndexes]), y, C = 10, cache_size=500,kernel="linear" )

R中SVM-RFE算法的实现

1 个答案: