Create synthetic cases for balancing training data

This function creates synthetic cases for balancing the training with an object of the class TEClassifierRegular or TEClassifierProtoNet.

Usage

get_synthetic_cases_from_matrix(
  matrix_form,
  times,
  features,
  target,
  sequence_length,
  method = c("smote"),
  min_k = 1,
  max_k = 6
)

matrix_form: Named matrix containing the text embeddings in a matrix form.
times: int for the number of sequences/times.
features: int for the number of features within each sequence.
target: Named factor containing the labels of the corresponding embeddings.
sequence_length: int Length of the text embedding sequences.
method: vector containing strings of the requested methods for generating new cases. Currently "smote", "dbsmote", and "adas" from the package smotefamily are available.
min_k: int The minimal number of nearest neighbors during sampling process.
max_k: int The maximum number of nearest neighbors during sampling process.

list with the following components:

syntetic_embeddings: Named data.frame containing the text embeddings of the synthetic cases.
syntetic_targets: Named factor containing the labels of the corresponding synthetic cases.
n_syntetic_units: table showing the number of synthetic cases for every label/category.