fmf {fmf} | R Documentation |
Fast Class Noise Detector with Multi-Factor-Based Learning
Description
This function computes the noise score for each observation
Usage
fmf(x, ...)
## S3 method for class 'formula'
fmf(formula, data, ...)
## Default S3 method:
fmf(
x,
knn = 5,
classColumn = 1,
boxplot_range = 1,
iForest = TRUE,
threads = 1,
...
)
Arguments
... |
optional parameters to be passed to other methods. |
formula |
a formula describing the classification variable and the attributes to be used. |
data , x |
data frame containing the tranining dataset to be filtered. |
knn |
total number of nearest neighbors to be used.The default is 5. |
classColumn |
positive integer indicating the column which contains the (factor of) classes. By default, a dataframe built from 'data' using the variables indicated in 'formula' and The first column is the response variable, thus no need to define the classColumn. |
boxplot_range |
range of box and whisker diagram. The dafault is 1. |
iForest |
compute iForest score or not. The dafault is TRUE. |
threads |
the number of cores to be used in parallel. |
Value
an object of class filter
, which is a list with four components:
-
cleanData
is a data frame containing the filtered dataset. -
remIdx
is a vector of integers indicating the indexes for removed instances (i.e. their row number with respect to the original data frame). -
noise_score
is a vector of values indicating the optential of being a noise. -
call
contains the original call to the filter.
Author(s)
Wanwan Zheng
Examples
data(iris)
out = fmf(Species~.,iris)