Just in time classifiers: Managing the slow drift case

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 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.