A Cascade Multiple Classifier System for Document Categorization
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
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
SVM-based just-in-time adaptive classifiers
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
On-line anomaly detection and resilience in classifier ensembles
Pattern Recognition Letters
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The stationarity requirement for the process generating the data is a common assumption in classifiers' design. When such hypothesis does not hold, e.g., in applications affected by aging effects, drifts, deviations, and faults, classifiers must react just in time, i.e., exactly when needed, to track the process evolution. The first step in designing effective just-in-time classifiers requires detection of the temporal instant associated with the process change, and the second one needs an update of the knowledge base used by the classification system to track the process evolution. This paper addresses the change detection aspect leaving the design of just-in-time adaptive classification systems to a companion paper. Two completely automatic tests for detecting nonstationarity phenomena are suggested, which neither require a priori information nor assumptions about the process generating the data. In particular, an effective computational intelligence-inspired test is provided to deal with multidimensional situations, a scenario where traditional change detection methods are generally not applicable or scarcely effective.