dfr_ecdd: Adapted EWMA for Concept Drift Detection (ECDD) method

In heimdall: Drift Adaptable Models

Adapted EWMA for Concept Drift Detection (ECDD) method

Description

ECDD is a concept change detection method that uses an exponentially weighted moving average (EWMA) chart to monitor the misclassification rate of an streaming classifier.

Usage

dfr_ecdd(lambda = 0.2, min_run_instances = 30, average_run_length = 100)

Arguments

lambda	The minimum number of instances before detecting change
min_run_instances	Necessary level for warning zone (2 standard deviation)
average_run_length	Necessary level for a positive drift detection

Value

dfr_ecdd object

Examples

library(daltoolbox)
library(heimdall)

# This example uses a dist-based drift detector with a synthetic dataset.

data(st_drift_examples)
data <- st_drift_examples$univariate
data$event <- NULL

model <- dfr_ecdd()

detection <- NULL
output <- list(obj=model, drift=FALSE)
for (i in 1:length(data$serie)){
 output <- update_state(output$obj, data$serie[i])
 if (output$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',]

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