Proceedings of the sixth international workshop on Machine learning
Instance-Based Learning Algorithms
Machine Learning
Robust classifiers without robust features
Neural Computation
C4.5: programs for machine learning
C4.5: programs for machine learning
Learning in the presence of concept drift and hidden contexts
Machine Learning
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Tracking Context Changes through Meta-Learning
Machine Learning - Special issue on multistrategy learning
Incremental Learning from Noisy Data
Machine Learning
COBBIT - A Control Procedure for COBWEB in the Presence of Concept Drift
ECML '93 Proceedings of the European Conference on Machine Learning
Effective Learning in Dynamic Environments by Explicit Context Tracking
ECML '93 Proceedings of the European Conference on Machine Learning
Exploiting Context When Learning to Classify
ECML '93 Proceedings of the European Conference on Machine Learning
Adapting to Drift in Continuous Domains (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
ML '92 Proceedings of the Ninth International Workshop on Machine Learning
Learning stable concepts in a changing world
PRICAI '96 Selected Papers from the Workshop on Reasoning with Incomplete and Changing Information and on Inducing Complex Representations: Learning and Reasoning with Complex Representations
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Learning and exploiting context in agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
CONTEXT '01 Proceedings of the Third International and Interdisciplinary Conference on Modeling and Using Context
VC-Dimension of a Context-Dependent Perceptron
CONTEXT '01 Proceedings of the Third International and Interdisciplinary Conference on Modeling and Using Context
Temporal Probabilistic Concepts from Heterogeneous Data Sequences
Soft-Ware 2002 Proceedings of the First International Conference on Computing in an Imperfect World
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
Relevant Data Expansion for Learning Concept Drift from Sparsely Labeled Data
IEEE Transactions on Knowledge and Data Engineering
Incremental rule learning based on example nearness from numerical data streams
Proceedings of the 2005 ACM symposium on Applied computing
Learning to predict train wheel failures
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Data streams classification by incremental rule learning with parameterized generalization
Proceedings of the 2006 ACM symposium on Applied computing
Association mining in time-varying domains
Intelligent Data Analysis
Incremental learning and concept drift in INTHELEX
Intelligent Data Analysis
Using multiple windows to track concept drift
Intelligent Data Analysis
Online classification of nonstationary data streams
Intelligent Data Analysis
A framework for generating data to simulate changing environments
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Learning in Environments with Unknown Dynamics: Towards more Robust Concept Learners
The Journal of Machine Learning Research
Handling Missing Data from Heteroskedastic and Nonstationary Data
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
The Cost of Learning Directed Cuts
ECML '07 Proceedings of the 18th European conference on Machine Learning
ADAPTIVE MACHINE LEARNING IN DELAYED FEEDBACK DOMAINS BY SELECTIVE RELEARNING
Applied Artificial Intelligence
An Ensemble of Classifiers for coping with Recurring Contexts in Data Streams
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Concept sampling: towards systematic selection in large-scale mixed concepts in machine learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Learning classifier systems: a complete introduction, review, and roadmap
Journal of Artificial Evolution and Applications
Learning, detecting, understanding, and predicting concept changes
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A case-based technique for tracking concept drift in spam filtering
Knowledge-Based Systems
Integrating learning and inference in multi-agent systems using cognitive context
MABS'06 Proceedings of the 2006 international conference on Multi-agent-based simulation VII
CALDS: context-aware learning from data streams
Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques
Tracking recurrent concepts using context
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Improving the learning of recurring concepts through high-level fuzzy contexts
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Learning recurring concepts from data streams with a context-aware ensemble
Proceedings of the 2011 ACM Symposium on Applied Computing
Identifying hidden contexts in classification
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Bayesian approach to the pattern recognition problem in nonstationary environment
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
Active learning with evolving streaming data
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
Context change detection for resource allocation in service-oriented systems
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Context-aware collaborative data stream mining in ubiquitous devices
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Learning about the learning process
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Mining Recurring Concept Drifts with Limited Labeled Streaming Data
ACM Transactions on Intelligent Systems and Technology (TIST)
Learning with local drift detection
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Improving the performance of data stream classifiers by mining recurring contexts
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
ACE: adaptive classifiers-ensemble system for concept-drifting environments
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Semi-supervised ensemble learning of data streams in the presence of concept drift
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
On-line bayesian context change detection in web service systems
Proceedings of the 2013 international workshop on Hot topics in cloud services
RCD: A recurring concept drift framework
Pattern Recognition Letters
A hidden Markov model for collaborative filtering
MIS Quarterly
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
Tracking recurrent concepts using context
Intelligent Data Analysis - Combined Learning Methods and Mining Complex Data
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Concept drift due to hidden changes in context complicates learningin many domains including financial prediction, medical diagnosis, andcommunication network performance. Existing machine learning approaches tothis problem use an incremental learning, on-line paradigm. Batch, off-linelearners tend to be ineffective in domains with hidden changes in context asthey assume that the training set is homogeneous. An off-line, meta-learning approach for the identification of hidden context ispresented. The new approach uses an existing batch learner and the processof {\it contextual clustering} to identify stable hiddencontexts and the associated context specific, locally stable concepts. Theapproach is broadly applicable to the extraction of context reflected intime and spatial attributes. Several algorithms for the approach arepresented and evaluated. A successful application of the approach to acomplex flight simulator control task is also presented.