Using cautious heuristics to bias generlization and guide example section
ACM SIGART Bulletin
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
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
Machine Learning - Special issue on context sensitivity and concept drift
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
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
An Analysis of Quantitative Measures Associated with Rules
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
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
Fuzzy classification trees for data analysis
Fuzzy Sets and Systems
The Knowledge Engineering Review
Incremental rule learning based on example nearness from numerical data streams
Proceedings of the 2005 ACM symposium on Applied computing
Data streams classification by incremental rule learning with parameterized generalization
Proceedings of the 2006 ACM symposium on Applied computing
Applying lazy learning algorithms to tackle concept drift in spam filtering
Expert Systems with Applications: An International Journal
Incremental learning and concept drift in INTHELEX
Intelligent Data Analysis
Using multiple windows to track concept drift
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
Efficient instance-based learning on data streams
Intelligent Data Analysis
Handling Missing Data from Heteroskedastic and Nonstationary Data
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Knowledge-based system for text classification using ID6NB algorithm
Knowledge-Based Systems
DynamicWEB: Adapting to Concept Drift and Object Drift in COBWEB
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
New ensemble methods for evolving data streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A Cascade Multiple Classifier System for Document Categorization
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Drift-Aware Ensemble Regression
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
An incremental learning algorithm for Lagrangian support vector machines
Pattern Recognition Letters
An empirical comparison of ID3 and back-propagation
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Classifiers: a theoretical and empirical study
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
A model updating strategy for predicting time series with seasonal patterns
Applied Soft Computing
IEEE Transactions on Neural Networks
Tracking Recurring Concepts with Meta-learners
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Learning, detecting, understanding, and predicting concept changes
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Incremental learning in nonstationary environments with controlled forgetting
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Proceedings of the 2010 conference on Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
A case-based technique for tracking concept drift in spam filtering
Knowledge-Based Systems
An ensemble approach for incremental learning in nonstationary environments
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Inference from aging information
IEEE Transactions on Neural Networks
Learning and representation change
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
Learning and representation change
AAAI'87 Proceedings of the sixth National conference on Artificial intelligence - Volume 2
Constructive induction on domain information
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Tree induction over perennial objects
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Dynamic financial distress prediction using instance selection for the disposal of concept drift
Expert Systems with Applications: An International Journal
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
Incremental learning with multi-level adaptation
Neurocomputing
Random ensemble decision trees for learning concept-drifting data streams
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Classification model for data streams based on similarity
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
Learning curve in concept drift while using active learning paradigm
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Mining Recurring Concept Drifts with Limited Labeled Streaming Data
ACM Transactions on Intelligent Systems and Technology (TIST)
Adaptive classifier selection based on two level hypothesis tests for incremental learning
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
ACE: adaptive classifiers-ensemble system for concept-drifting environments
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
On-Line learning of decision trees in problems with unknown dynamics
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
User action based adaptive learning with weighted bayesian classification for filtering spam mail
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Handling different categories of concept drifts in data streams using distributed GP
EuroGP'10 Proceedings of the 13th European conference on Genetic Programming
Very Fast Decision Rules for multi-class problems
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Learning decision rules from data streams
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Inverse matrix-free incremental proximal support vector machine
Decision Support Systems
Learning in non-stationary environments with class imbalance
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Classification of Unseen Examples under Uncertainty
Fundamenta Informaticae
Handling time changing data with adaptive very fast decision rules
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Learning user preferences for adaptive pervasive environments: An incremental and temporal approach
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
Just-in-time adaptive similarity component analysis in nonstationary environments
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Hi-index | 0.00 |
Induction of a concept description given noisy instances is difficult and is further exacerbated when the concepts may change over time. This paper presents a solution which has been guided by psychological and mathematical results. The method is based on a distributed concept description which is composed of a set of weighted, symbolic characterizations. Two learning processes incrementally modify this description. One adjusts the characterization weights and another creates new characterizations. The latter process is described in terms of a search through the space of possibilities and is shown to require linear space with respect to the number of attribute-value pairs in the description language. The method utilizes previously acquired concept definitions in subsequent learning by adding an attribute for each learned concept to instance descriptions. A program called STAGGER fully embodies this method, and this paper reports on a number of empirical analyses of its performance. Since understanding the relationships between a new learning method and existing ones can be difficult, this paper first reviews a framework for discussing machine learning systems and then describes STAGGER in that framework.