Just-in-Time Adaptive Classifiers - Part II: Designing the Classifier

Aging effects, environmental changes, thermal drifts, and soft and hard faults affect physical systems by changing their nature and behavior over time. To cope with a process evolution adaptive solutions must be envisaged to track its dynamics; in this direction, adaptive classifiers are generally designed by assuming the stationary hypothesis for the process generating the data with very few results addressing nonstationary environments. This paper proposes a methodology based on -nearest neighbor (NN) classifiers for designing adaptive classification systems able to react to changing conditions just-in-time (JIT), i.e., exactly when it is needed. -NN classifiers have been selected for their computational-free training phase, the possibility to easily estimate the model complexity and keep under control the computational complexity of the classifier through suitable data reduction mechanisms. A JIT classifier requires a temporal detection of a (possible) process deviation (aspect tack...

• Type: Journal
• Year: 2008
• Where: TNN
• Authors: Cesare Alippi, Manuel Roveri
• Keywords: Adaptive, Adaptive Classification Systems, Evolution Adaptive Solutions, TNN 2008