A survey on concept drift adaptation

2014/03/01 by João Gama, Indrė Žliobaitė, Albert Bifet +2 · 8 citations
Computer Science · Engineering · #Data Stream Mining Techniques #Spam and Phishing Detection #Innovative Microfluidic and Catalytic Techniques Innovation #Concept drift #Computer science #Categorization #Adaptation (eye) #Relation (database) #Set (abstract data type) #Machine learning #Artificial intelligence #Adaptive learning #Variable (mathematics) #Data science #Data mining #Data stream mining

paper · doi:10.1145/2523813

openalex publication_date 2014/03/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

Concept drift primarily refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time. Assuming a general knowledge of supervised learning in this article, we characterize adaptive learning processes; categorize existing strategies for handling concept drift; overview the most representative, distinct, and popular techniques and algorithms; discuss evaluation methodology of adaptive algorithms; and present a set of illustrative applications. The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art. Thus, it aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners.

Citations

• Classifier Ensembles for Changing Environments
• Learning with Drift Detection
• A unifying view on dataset shift in classification
• The perceptron: A probabilistic model for information storage and organization in the brain.
• CONTINUOUS INSPECTION SCHEMES
• Note on the Sampling Error of the Difference Between Correlated Proportions or Percentages
• Random sampling with a reservoir
• Anomaly detection
• Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm
• Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
• Classification And Regression Trees

Cited by

• What the Detector Can See: Evaluating CPS Anomaly Detectors Independently of the Decision Rule
• Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detection
• A Counterfactual Diagnostic Framework for Explaining KS Deterioration in Credit Risk Model Validation
• WritePolicyBench: Benchmarking Memory Write Policies under Byte Budgets
• Ensemble learning for data stream analysis: A survey
• Learning under Concept Drift: A Review
• Challenges in Benchmarking Stream Learning Algorithms with Real-world Data
• Concept drift [wikipedia]

Related

• Learning under Concept Drift: an Overview
• Learning under Concept Drift: A Review
• Understanding Concept Drift
• Ensuring Learning Guarantees on Concept Drift Detection with Statistical Learning Theory
• A comprehensive analysis of concept drift locality in data streams