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IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 19, NO. 7, JULY 2008 1145 Just-in-Time Adaptive Classifiers--Part I
 

Summary: IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 19, NO. 7, JULY 2008 1145
Just-in-Time Adaptive Classifiers--Part I:
Detecting Nonstationary Changes
Cesare Alippi, Fellow, IEEE, and Manuel Roveri
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

  

Source: Alippi, Cesare - Dipartimento di Elettronica e Informazione, Politecnico di Milano

 

Collections: Computer Technologies and Information Sciences