be: Backward Elimination Feature Selection with Random KNN
In rknn: Random KNN Classification and Regression

Recursive Backward Elimination Feature Selection with Random KNN

rknnBeg(data, y, k = 1, r = 500, mtry = trunc(sqrt(ncol(data))),
  fixed.partition = FALSE, pk = 0.5, stopat = 4, cluster=NULL, seed = NULL)
rknnBel(data, y, k = 1, r = 500, mtry = trunc(sqrt(ncol(data))),
  fixed.partition = FALSE, d = 1, stopat = 4,  cluster=NULL, seed = NULL)

`data`	An n x p numeric design matrix.
`y`	A vector of responses. For a numeric vector, Random Knn regression is performed. For a factor, Random classification is performed.
`k`	An integer for the number of nearest neighbors.
`r`	An integer for the number of base KNN models.
`mtry`	Number of features to be drawn for each KNN.
`fixed.partition`	Logical. Use fixed partition of dynamic partition of the data into training and testing subsets for each KNN.
`pk`	A real number between 0 and to indicate the proportion of the feature set to be kept in each step.
`d`	A integer to indicate the number of features to be dropped in each step.
`stopat`	an integer for the minimum number of variables.
`cluster`	An object of class ‘c("SOCKcluster", "cluster")’
`seed`	An integer seed.