dfr_eddm {heimdall}

Adapted Early Drift Detection Method (EDDM) method

Description

EDDM (Early Drift Detection Method) aims to improve the detection rate of gradual concept drift in DDM, while keeping a good performance against abrupt concept drift. doi:2747577a61c70bc3874380130615e15aff76339e

Usage

dfr_eddm(
  min_instances = 30,
  min_num_errors = 30,
  warning_level = 0.95,
  out_control_level = 0.9
)

Arguments

min_instances

The minimum number of instances before detecting change

min_num_errors

The minimum number of errors before detecting change

warning_level

Necessary level for warning zone

out_control_level

Necessary level for a positive drift detection

Value

dfr_eddm object

Examples

library(daltoolbox)
library(heimdall)

# This example uses an error-based drift detector with a synthetic a 
# model residual where 1 is an error and 0 is a correct prediction.

data(st_drift_examples)
data <- st_drift_examples$univariate
data$event <- NULL
data$prediction <- st_drift_examples$univariate$serie > 4

model <- dfr_eddm()

detection <- NULL
output <- list(obj=model, drift=FALSE)
for (i in 1:length(data$prediction)){
 output <- update_state(output$obj, data$prediction[i])
 if (output$drift){
   type <- 'drift'
   output$obj <- reset_state(output$obj)
 }else{
   type <- ''
 }
 detection <- rbind(detection, data.frame(idx=i, event=output$drift, type=type))
}

detection[detection$type == 'drift',]