fmf: Fast Class Noise Detector with Multi-Factor-Based Learning

In fmf: 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)

fmf documentation built on Sept. 3, 2020, 9:07 a.m.