smotefamily (version 1.3.1)

SMOTE: Synthetic Minority Oversampling TEchnique

Description

Generate synthetic positive instances using SMOTE algorithm

Usage

SMOTE(X, target, K = 5, dup_size = 0)

Arguments

X

A data frame or matrix of numeric-attributed dataset

target

A vector of a target class attribute corresponding to a dataset X.

K

The number of nearest neighbors during sampling process

dup_size

The number or vector representing the desired times of synthetic minority instances over the original number of majority instances

Value

data

A resulting dataset consists of original minority instances, synthetic minority instances and original majority instances with a vector of their respective target class appended at the last column

syn_data

A set of synthetic minority instances with a vector of minority target class appended at the last column

orig_N

A set of original instances whose class is not oversampled with a vector of their target class appended at the last column

orig_P

A set of original instances whose class is oversampled with a vector of their target class appended at the last column

K

The value of parameter K for nearest neighbor process used for generating data

K_all

Unavailable for this method

dup_size

The maximum times of synthetic minority instances over original majority instances in the oversampling

outcast

Unavailable for this method

eps

Unavailable for this method

method

The name of oversampling method used for this generated dataset (SMOTE)

References

Chawla, N., Bowyer, K., Hall, L. and Kegelmeyer, W. 2002. SMOTE: Synthetic minority oversampling technique. Journal of Artificial Intelligence Research. 16, 321-357.

Examples

Run this code
# NOT RUN {
    data_example = sample_generator(10000,ratio = 0.80)
	genData = SMOTE(data_example[,-3],data_example[,3])
	genData_2 = SMOTE(data_example[,-3],data_example[,3],K=7)

# }

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