# Backward Elimination Feature Selection with Random KNN

### Description

Recursive Backward Elimination Feature Selection with Random KNN

### Usage

1 2 3 4 |

### Arguments

`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. |

### Author(s)

Shengqiao Li<lishengqiao@yahoo.com>

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