fmf (version 1.1.1)

Fast Class Noise Detector with Multi-Factor-Based Learning

Description

A fast class noise detector which provides noise score for each observations. The package takes advantage of 'RcppArmadillo' to speed up the calculation of distances between observations.

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Version

Install

install.packages('fmf')

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Version

1.1.1

License

MIT + file LICENSE

Maintainer

Wanwan Zheng

Last Published

September 3rd, 2020

Functions in fmf (1.1.1)

normalized

this function normalizes the data

normalization

The Max-Min Normalization

kernelknn

kernel k-nearest-neighbors

ozone

Ozone Level Detection Data Set

tuning

Tuning For Fast Class Noise Detector with Multi-Factor-Based Learning

iris

Iris Data Set

func_tbl_dist

this function returns the probabilities in case of classification

plot

PCA Plot of the Noise Score of Each Individual

regr_folds

create folds (in regression) [ detailed information about class_folds in the FeatureSelection package ]

func_categorical_preds

OPTION to convert categorical features TO either numeric [ if levels more than 32] OR to dummy variables [ if levels less than 32 ]

entropy

this function compute entropy

func_tbl

this function returns a table of probabilities for each label

func_shuffle

shuffle data

fmf

Fast Class Noise Detector with Multi-Factor-Based Learning

class_folds

stratified folds (in classification) [ detailed information about class_folds in the FeatureSelection package ]

switch.ops

Arithmetic operations on lists

FUNCTION_weights

this function is used as a kernel-function-identifier [ takes the distances and a weights-kernel (in form of a function) and returns weights ]

australian

Australian Credit Approval