Evolving pattern recognition systems
IEEE Transactions on Evolutionary Computation
Adaptive fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Underwater target classification in changing environments using an adaptive feature mapping
IEEE Transactions on Neural Networks
A temporally adaptive classifier for multispectral imagery
IEEE Transactions on Neural Networks
Adaptive probabilistic neural networks for pattern classification in time-varying environment
IEEE Transactions on Neural Networks
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 fuzzy ARTMAP nonparametric probability estimator for nonstationary pattern recognition problems
IEEE Transactions 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
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A classifier expected to work in a non-stationary environment has to: (i) detect changes in the process generating the data; (ii) suitably react to the change by adapting to the new working condition. Just-In-Time Adaptive classifiers, a classification structure addressing stationary and nonstationary conditions, have been recently presented to the computational intelligence community. Such classifiers require a temporal detection of a (possible) process deviation followed by an adaptive management of the knowledge base characterizing the classifier to cope with the process change. This paper improves Just-in-time Adaptive Classifiers by integrating temporal information about the state of the process under monitoring. An index for the process deviation is defined which, coupled with an adaptive weighted k-NN classifier, shows to be particularly effective in dealing with smooth process drifts and ageing phenomena.