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
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Just in time classifiers: managing the slow drift case
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A new method for varying adaptive bandwidth selection
IEEE Transactions on Signal Processing
Just-in-Time Adaptive Classifiers—Part I: Detecting Nonstationary Changes
IEEE Transactions on Neural Networks
Just-in-Time Adaptive Classifiers—Part II: Designing the Classifier
IEEE Transactions on Neural Networks
A distributed self-adaptive nonparametric change-detection test for sensor/actuator networks
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
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Classification systems meant to operate in non-stationary environments are requested to adapt when the process generating the observed data changes. A particularly effective form of adaptation in the abrupt perturbation case suggests to release the obsolete knowledge base of the classifier (or training set), and consider novel samples to configure the new classification model. In this direction, we propose an adaptive classifier based on a change detection test used both for detecting changes in the process and identifying the new training set (and, then, the new classifier). A key point of the proposed solution 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 generating the data evolves through a sequence of abrupt changes.