The stationarity requirement for the process gener- ating 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 de- tection 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 phe- nomena are suggested, which neither require a priori information nor assumptions about the process generating the data. In partic- ular, an effective computational intelligence-inspired test is pro- vided to deal with multidimensional situations, a scenario where traditional change detection methods are generally not applicable or scarcely effective.

Just-in-time Adaptive Classifiers. Part I. Detecting non-stationary Changes

ALIPPI, CESARE;ROVERI, MANUEL
2008-01-01

Abstract

The stationarity requirement for the process gener- ating 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 de- tection 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 phe- nomena are suggested, which neither require a priori information nor assumptions about the process generating the data. In partic- ular, an effective computational intelligence-inspired test is pro- vided to deal with multidimensional situations, a scenario where traditional change detection methods are generally not applicable or scarcely effective.
2008
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/527268
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