Generate synthetic positive instances using SMOTE algorithm

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

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

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

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

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

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

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

Unavailable for this method

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

Unavailable for this method

Unavailable for this method

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

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

# 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) # }