C4.5: programs for machine learning
C4.5: programs for machine learning
The weighted majority algorithm
Information and Computation
Learning in the presence of concept drift and hidden contexts
Machine Learning
Machine Learning
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
The application of AdaBoost for distributed, scalable and on-line learning
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
Incremental Learning with Partial Instance Memory
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Online Ensemble Learning: An Empirical Study
Machine Learning
Incremental learning with partial instance memory
Artificial Intelligence
An empirical evaluation of bagging and boosting
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Incremental rule learning based on example nearness from numerical data streams
Proceedings of the 2005 ACM symposium on Applied computing
Combining proactive and reactive predictions for data streams
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Using additive expert ensembles to cope with concept drift
ICML '05 Proceedings of the 22nd international conference on Machine learning
Data streams classification by incremental rule learning with parameterized generalization
Proceedings of the 2006 ACM symposium on Applied computing
Effective classification of noisy data streams with attribute-oriented dynamic classifier selection
Knowledge and Information Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cross-domain video concept detection using adaptive svms
Proceedings of the 15th international conference on Multimedia
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
Dynamic integration of classifiers for handling concept drift
Information Fusion
An active learning system for mining time-changing data streams
Intelligent Data Analysis
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Efficient Tracking as Linear Program on Weak Binary Classifiers
Proceedings of the 30th DAGM symposium on Pattern Recognition
Mining Concept-Drifting Data Streams with Multiple Semi-Random Decision Trees
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Conceptual equivalence for contrast mining in classification learning
Data & Knowledge Engineering
Mobile Networks and Applications
Mining decision rules on data streams in the presence of concept drifts
Expert Systems with Applications: An International Journal
Class Specific Fuzzy Decision Trees for Mining High Speed Data Streams
Fundamenta Informaticae
Data Mining and Knowledge Discovery
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Drift-Aware Ensemble Regression
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Ambiguous decision trees for mining concept-drifting data streams
Pattern Recognition Letters
Closed loop knowledge discovery for decision support in intensive care medicine
ICCOMP'09 Proceedings of the WSEAES 13th international conference on Computers
Adaptive knowledge discovery for decision support in intensive care units
WSEAS Transactions on Computers
Ensembles in adversarial classification for spam
Proceedings of the 18th ACM conference on Information and knowledge management
Regression Trees from Data Streams with Drift Detection
DS '09 Proceedings of the 12th International Conference on Discovery Science
Transfer Learning beyond Text Classification
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Mining Multi-label Concept-Drifting Data Streams Using Dynamic Classifier Ensemble
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Incremental learning in nonstationary environments with controlled forgetting
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
SERA: selectively recursive approach towards nonstationary imbalanced stream data mining
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Using diversity to handle concept drift in on-line learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Collaborative filtering with temporal dynamics
Communications of the ACM
A case-based technique for tracking concept drift in spam filtering
Knowledge-Based Systems
A framework for modeling positive class expansion with single snapshot
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Mining multi-label concept-drifting data streams using ensemble classifiers
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Greedy optimization classifiers ensemble based on diversity
Pattern Recognition
Detecting drifts in multi-issue negotiations
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
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
Incremental learning with multi-level adaptation
Neurocomputing
Gas sensor drift mitigation using classifier ensembles
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
Editorial: Classifying text streams by keywords using classifier ensemble
Data & Knowledge Engineering
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
Tracking concept change with incremental boosting by minimization of the evolving exponential loss
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Mining data streams with concept drifts using genetic algorithm
Artificial Intelligence Review
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
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
Classifier ensemble for uncertain data stream classification
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
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
Learning in non-stationary environments with class imbalance
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Class Specific Fuzzy Decision Trees for Mining High Speed Data Streams
Fundamenta Informaticae
A multi-choice offer strategy for bilateral multi-issue negotiations using modified DWM learning
Proceedings of the 13th International Conference on Electronic Commerce
A new method of mining data streams using harmony search
Journal of Intelligent Information Systems
Comparison of long-term adaptivity for neural networks
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
A social approach for learning agents
Expert Systems with Applications: An International Journal
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
A survey of multiple classifier systems as hybrid systems
Information Fusion
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Algorithms for tracking concept drift are important formany applications. We present a general method basedon the Weighted Majority algorithm for using any on-linelearner for concept drift. Dynamic Weighted Majority(DWM) maintains an ensemble of base learners, predictsusing a weighted-majority vote of these "experts",and dynamically creates and deletes experts in response tochanges in performance. We empirically evaluated two experimentalsystems based on the method using incrementalnaive Bayes and Incremental Tree Inducer (ITI) as experts.For the sake of comparison, we also included Blum's implementationof Weighted Majority. On the STAGGER Conceptsand on the SEA Concepts, results suggest that the ensemblemethod learns drifting concepts almost as well as the basealgorithms learn each concept individually. Indeed, we reportthe best overall results for these problems to date.