Instance-Based Learning Algorithms
Machine Learning
Robust classifiers without robust features
Neural Computation
Experience with a learning personal assistant
Communications of the ACM
Learning in the presence of concept drift and hidden contexts
Machine 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
Case-Based Initialization of Genetic Algorithms
Proceedings of the 5th International Conference on Genetic Algorithms
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Machine Learning - Special issue on context sensitivity and concept drift
An adaptive algorithm for learning changes in user interests
Proceedings of the eighth international conference on Information and knowledge management
Selecting Examples for Partial Memory Learning
Machine Learning
Learning and exploiting context in agents
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 3
Refined Time Stamps for Concept Drift Detection During Mining for Classification Rules
TSDM '00 Proceedings of the First International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining-Revised Papers
The Pragmatic Roots of Context
CONTEXT '99 Proceedings of the Second International and Interdisciplinary Conference on Modeling and Using Context
CONTEXT '01 Proceedings of the Third International and Interdisciplinary Conference on Modeling and Using Context
Classification of Customer Call Data in the Presence of Concept Drift and Noise
Soft-Ware 2002 Proceedings of the First International Conference on Computing in an Imperfect World
Incremental Learning with Partial Instance Memory
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
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
Introduction to the Special Issue on Meta-Learning
Machine Learning
Incremental learning with partial instance memory
Artificial Intelligence
Relevant Data Expansion for Learning Concept Drift from Sparsely Labeled Data
IEEE Transactions on Knowledge and Data Engineering
Association mining in time-varying domains
Intelligent Data Analysis
Using multiple windows to track concept drift
Intelligent Data Analysis
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
ADAPTIVE MACHINE LEARNING IN DELAYED FEEDBACK DOMAINS BY SELECTIVE RELEARNING
Applied Artificial Intelligence
Meta-learning optimal parameter values in non-stationary environments
Knowledge-Based Systems
Mobile Networks and Applications
Learning, detecting, understanding, and predicting concept changes
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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
Adaptive methods for classification in arbitrarily imbalanced and drifting data streams
PAKDD'09 Proceedings of the 13th Pacific-Asia international conference on Knowledge discovery and data mining: new frontiers in applied data mining
Exploiting concept clumping for efficient incremental e-mail categorization
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
Learning recurring concepts from data streams with a context-aware ensemble
Proceedings of the 2011 ACM Symposium on Applied Computing
Beating the baseline prediction in food sales: How intelligent an intelligent predictor is?
Expert Systems with Applications: An International Journal
A User-Centric Approach for Personalized Service Provisioning in Pervasive Environments
Wireless Personal Communications: An International Journal
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
What is context and how can an agent learn to find and use it when making decisions?
CEEMAS'05 Proceedings of the 4th international Central and Eastern European conference on Multi-Agent Systems and Applications
Tracking the preferences of users using weak estimators
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
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
| Hi-index | 0.00 |
The article deals with the problem of learning incrementally(‘on-line’) in domains where the target concepts arecontext-dependent, so that changes in context can produce more orless radical changes in the associated concepts. In particular, weconcentrate on a class of learning tasks where the domain providesexplicit clues as to the current context (e.g.,attributes with characteristic values). A general two-level learning model ispresented that effectively adjusts to changing contexts by trying todetect (via ‘meta-learning’) contextual clues and using thisinformation to focus the learning process. Context learning anddetection occur during regular on-line learning, withoutseparate training phases for context recognition. Two operationalsystems based on this model are presented that differ in theunderlying learning algorithm and in the way they use contextualinformation: METAL(B) combines meta-learning with a Bayesianclassifier, while METAL(IB) is based on an instance-based learningalgorithm. Experiments with synthetic domains as well as a number of‘real-world” problems show that the algorithms are robust in avariety of dimensions, and that meta-learning can produce substantialincreases in accuracy over simple object-level learning in situationswith changing contexts.