dfr_aedd: Autoencoder-Based Drift Detection method

In heimdall: Drift Adaptable Models

Autoencoder-Based Drift Detection method

Description

Autoencoder-Based method for concept drift detection doi:0.1109/ICDMW58026.2022.00109.

Usage

dfr_aedd(
  encoding_size,
  ae_class = autoenc_encode_decode,
  batch_size = 32,
  num_epochs = 1000,
  learning_rate = 0.001,
  window_size = 100,
  monitoring_step = 1700,
  criteria = "mann_whitney",
  alpha = 0.01,
  reporting = FALSE
)

Arguments

encoding_size	Encoding Size
ae_class	Autoencoder Class
batch_size	Batch Size for batch learning
num_epochs	Number of Epochs for training
learning_rate	Learning Rate
window_size	Size of the most recent data to be used
monitoring_step	The number of rows that the drifter waits to be is updated
criteria	The method to be used to check if there is a drift. May be mann_whitney (default), kolmogorov_smirnov, levene
alpha	The significance threshold for the statistical test used in criteria
reporting	If TRUE, some data are returned as norm_x_oh, drift_input, hist_proj, and recent_proj.

Value

dfr_aedd object

Examples

#See an example of using `dfr_aedd` at this
#https://github.com/cefet-rj-dal/heimdall/blob/main/multivariate/dfr_aedd.md

heimdall documentation built on June 8, 2025, 11:11 a.m.