Random sampling with a reservoir
ACM Transactions on Mathematical Software (TOMS)
Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
The weighted majority algorithm
Information and Computation
Tracking Drifting Concepts By Minimizing Disagreements
Machine Learning - Special issue on computational learning theory
Learning in the presence of concept drift and hidden contexts
Machine Learning
Tolerating Concept and Sampling Shift in Lazy Learning UsingPrediction Error Context Switching
Artificial Intelligence Review - Special issue on lazy learning
Tracking Context Changes through Meta-Learning
Machine Learning - Special issue on multistrategy learning
A game of prediction with expert advice
Journal of Computer and System Sciences - Special issue on the eighth annual workshop on computational learning theory, July 5–8, 1995
Machine Learning - Special issue on context sensitivity and concept drift
Machine Learning - Special issue on context sensitivity and concept drift
Handling concept drifts in incremental learning with support vector machines
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
The impact of changing populations on classifier performance
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Selecting Examples for Partial Memory Learning
Machine Learning
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Maintaining Stream Statistics over Sliding Windows
SIAM Journal on Computing
RainForest—A Framework for Fast Decision Tree Construction of Large Datasets
Data Mining and Knowledge Discovery
User Modeling for Adaptive News Access
User Modeling and User-Adapted Interaction
Incremental Learning from Noisy Data
Machine Learning
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Effective Learning in Dynamic Environments by Explicit Context Tracking
ECML '93 Proceedings of the European Conference on Machine Learning
SLIQ: A Fast Scalable Classifier for Data Mining
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Estimating the Generalization Performance of an SVM Efficiently
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An Interval Classifier for Database Mining Applications
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Tracking Changing User Interests through Prior-Learning of Context
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
A Method for Partial-Memory Incremental Learning and its Application to Computer Intrusion Detection
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Online ensemble learning
Online Ensemble Learning: An Empirical Study
Machine Learning
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Incremental learning with partial instance memory
Artificial Intelligence
On Change Diagnosis in Evolving Data Streams
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
ACM SIGMOD Record
Using additive expert ensembles to cope with concept drift
ICML '05 Proceedings of the 22nd international conference on Machine learning
Prediction, Learning, and Games
Prediction, Learning, and Games
Tackling concept drift by temporal inductive transfer
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Single-pass online learning: performance, voting schemes and online feature selection
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
On biased reservoir sampling in the presence of stream evolution
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Mining in Anticipation for Concept Change: Proactive-Reactive Prediction in Data Streams
Data Mining and Knowledge Discovery
Entropy-based Concept Shift Detection
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Stanley: The robot that won the DARPA Grand Challenge: Research Articles
Journal of Robotic Systems - Special Issue on the DARPA Grand Challenge, Part 2
A note on the utility of incremental learning
AI Communications
Decision trees for mining data streams
Intelligent Data Analysis
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Adaptive-Size Reservoir Sampling over Data Streams
SSDBM '07 Proceedings of the 19th International Conference on Scientific and Statistical Database Management
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Using multiple windows to track concept drift
Intelligent Data Analysis
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Sequential Change Detection on Data Streams
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Boosting classifiers for drifting concepts
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Categorizing and mining concept drifting data streams
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
StreamKrimp: Detecting Change in Data Streams
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Adaptive Learning Rate for Online Linear Discriminant Classifiers
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Paired Learners for Concept Drift
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
ACM Computing Surveys (CSUR)
Sketching Sampled Data Streams
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
New ensemble methods for evolving data streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
On the window size for classification in changing environments
Intelligent Data Analysis
Adaptive concept drift detection
Statistical Analysis and Data Mining - Best of SDM'09
Determining the Training Window for Small Sample Size Classification with Concept Drift
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Weighted random sampling with a reservoir
Information Processing Letters
Collaborative filtering with temporal dynamics
Communications of the ACM
A case-based technique for tracking concept drift in spam filtering
Knowledge-Based Systems
Tracking recurring contexts using ensemble classifiers: an application to email filtering
Knowledge and Information Systems
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift
IEEE Transactions on Knowledge and Data Engineering
Adaptive bayes for a student modeling prediction task based on learning styles
UM'03 Proceedings of the 9th international conference on User modeling
Detecting concept drift using statistical testing
DS'07 Proceedings of the 10th international conference on Discovery science
Change detection in learning histograms from data streams
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
A test paradigm for detecting changes in transactional data streams
DASFAA'08 Proceedings of the 13th international conference on Database systems for advanced applications
Transfer estimation of evolving class priors in data stream classification
Pattern Recognition
Online mass flow prediction in CFB boilers with explicit detection of sudden concept drift
ACM SIGKDD Explorations Newsletter
Knowledge Discovery from Data Streams
Knowledge Discovery from Data Streams
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
GNUsmail: Open Framework for On-line Email Classification
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Leveraging bagging for evolving data streams
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Sentiment knowledge discovery in twitter streaming data
DS'10 Proceedings of the 13th international conference on Discovery science
Learning model trees from evolving data streams
Data Mining and Knowledge Discovery
Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints
IEEE Transactions on Knowledge and Data Engineering
Learning recurring concepts from data streams with a context-aware ensemble
Proceedings of the 2011 ACM Symposium on Applied Computing
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Process Mining: Discovery, Conformance and Enhancement of Business Processes
Incremental learning with multi-level adaptation
Neurocomputing
Fuzzy classification in dynamic environments
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Recent progress in natural computation and knowledge discovery
Combining similarity in time and space for training set formation under concept drift
Intelligent Data Analysis
A unifying view on dataset shift in classification
Pattern Recognition
MOA: a real-time analytics open source framework
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Beating the baseline prediction in food sales: How intelligent an intelligent predictor is?
Expert Systems with Applications: An International Journal
Controlled permutations for testing adaptive classifiers
DS'11 Proceedings of the 14th international conference on Discovery science
Learning about the learning process
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Exponentially weighted moving average charts for detecting concept drift
Pattern Recognition Letters
Kalman filters and adaptive windows for learning in data streams
DS'06 Proceedings of the 9th international conference on Discovery Science
Fast perceptron decision tree learning from evolving data streams
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
DDD: A New Ensemble Approach for Dealing with Concept Drift
IEEE Transactions on Knowledge and Data Engineering
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Incremental Learning of Concept Drift in Nonstationary Environments
IEEE Transactions on Neural Networks
Robust tracking with weighted online structured learning
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Next challenges for adaptive learning systems
ACM SIGKDD Explorations Newsletter
Online techniques for dealing with concept drift in process mining
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
On evaluating stream learning algorithms
Machine Learning
Change Detection in Streaming Multivariate Data Using Likelihood Detectors
IEEE Transactions on Knowledge and Data Engineering
Knowledge-Based Systems
Predictive Handling of Asynchronous Concept Drifts in Distributed Environments
IEEE Transactions on Knowledge and Data Engineering
Recent and robust query auto-completion
Proceedings of the 23rd international conference on World wide web
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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.