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
Predictive learning models for concept drift
Theoretical Computer Science - Algorithmic learning theory
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 from Noisy Data
Machine Learning
Online ensemble learning
Incremental learning with partial instance memory
Artificial Intelligence
Adaptive anomaly detection with evolving connectionist systems
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
Real-time data mining of non-stationary data streams from sensor networks
Information Fusion
ACE: adaptive classifiers-ensemble system for concept-drifting environments
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Adaptive probabilistic neural networks for pattern classification in time-varying environment
IEEE Transactions on Neural Networks
A Cascade Multiple Classifier System for Document Categorization
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
IEEE Transactions on Neural Networks
Classifier ensembles for virtual concept drift - the DEnBoost algorithm
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Bayesian approach to the pattern recognition problem in nonstationary environment
PReMI'11 Proceedings of the 4th international conference on Pattern recognition and machine intelligence
A dynamic logistic multiple classifier system for online classification
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Affective modeling from multichannel physiology: analysis of day differences
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Online non-stationary boosting
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
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We describe an ensemble of classifiers based algorithm for incremental learning in nonstationary environments. In this formulation, we assume that the learner is presented with a series of training datasets, each of which is drawn from a different snapshot of a distribution that is drifting at an unknown rate. Furthermore, we assume that the algorithm must learn the new environment in an incremental manner, that is, without having access to previously available data. Instead of a time window over incoming instances, or an aged based forgetting - as used by most ensemble based nonstationary learning algorithms - a strategic weighting mechanism is employed that tracks the classifiers' performances over drifting environments to determine appropriate voting weights. Specifically, the proposed approach generates a single classifier for each dataset that becomes available, and then combines them through a dynamically modified weighted majority voting, where the voting weights themselves are computed as weighted averages of classifiers' individual performances over all environments. We describe the implementation details of this approach, as well as its initial results on simulated non-stationary environments.