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
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive Window Size Image De-noising Based on Intersection of Confidence Intervals (ICI) Rule
Journal of Mathematical Imaging and Vision
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
A Low-Granularity Classifier for Data Streams with Concept Drifts and Biased Class Distribution
IEEE Transactions on Knowledge and Data Engineering
Learning, detecting, understanding, and predicting concept changes
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Just in time classifiers: managing the slow drift case
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Adaptive classifiers with ICI-based adaptive knowledge base management
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
A new method for varying adaptive bandwidth selection
IEEE Transactions on Signal Processing
A spatially adaptive nonparametric regression image deblurring
IEEE Transactions on Image Processing
Pointwise Shape-Adaptive DCT for High-Quality Denoising and Deblocking of Grayscale and Color Images
IEEE Transactions on Image Processing
Just-in-Time Adaptive Classifiers—Part II: Designing the Classifier
IEEE Transactions on Neural Networks
SVM-based just-in-time adaptive classifiers
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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Classification systems meant to operate in nonstationary environments are requested to adapt when the process generating the observed data changes. A straightforward form of adaptation implementing the instance selection approach suggests releasing the obsolete data onto which the classifier is configured by replacing it with novel samples before retraining. In this direction, we propose an adaptive classifier based on the intersection of confidence intervals rule for detecting a possible change in the process generating the data as well as identifying the new data to be used to configure the classifier. A key point of the research is that no assumptions are made about the distribution of the process generating the data. Experimental results show that the proposed adaptive classification system is particularly effective in situations where the process is subject to abrupt changes.