Unsupervised concept drift detection for multi-label data streams

Abstract

Many real-world applications adopt multi-label data streams as the need for algorithms to deal with rapidly changing data increases. Changes in data distribution, also known as concept drift, cause existing classification models to rapidly lose their effectiveness. To assist the classifiers, we propose a novel algorithm called Label Dependency Drift Detector (LD3), an unsupervised concept drift detector using label dependencies within the data for multi-label data streams. Our study exploits the dynamic temporal dependencies between labels using a label influence ranking method, which leverages a data fusion algorithm and uses the produced ranking to detect concept drift. LD3 is the first unsupervised concept drift detection algorithm in the multi-label classification problem area. In this study, we perform an extensive evaluation of LD3 by comparing it with 14 prevalent supervised concept drift detection algorithms that we adapt to the problem area using 15 datasets and a baseline classifier. The results show that LD3 provides between 16.9 and 56% better predictive performance than comparable detectors on both real-world and synthetic data streams.

A Novel Edge Architecture and Solution for Detecting Concept Drift in Smart Environments

“…Much of the existing literature proposes concept drift detection and approaches. However, these methods are mainly exploited using synthetic and real-world static datasets [99][100][101], unlike in the case of real-world data streams where changes in data distribution are often expected, and guaranteeing the correctness of the concept drift is a great challenge. Several approaches are available to ensure the correctness of detected concept drift.…”

Remote Health Monitoring Systems for Elderly People: A Survey

“…This is known a Model or Concept drift in different systems [ 116 ]. Many of the drift detection approaches has been proposed in different streams of applications [ 117 , 118 , 119 ]; however, much less in stream of RHMS.…”

An Experimental Review of the Ensemble-Based Data Stream Classification Algorithms in Non-Stationary Environments

“…Real concept drift may occur with or without change in the P (X). In addition, concept drifts can occur at different occurrence rates, which result in four drift types: Sudden, Gradual, Incremental, and Recurrent (Gulcan and Can, 2023).…”