title: A just-in-time adaptive classification system based on the intersection of confidence intervals rule.
abstract: Classification systems meant to operate in nonstationary environments are requested to adapt when the process generating the observed data changes. A straightforward form of adaptation implementing the instance selection approach suggests releasing the obsolete data onto which the classifier is configured by replacing it with novel samples before retraining. In this direction, we propose an adaptive classifier based on the intersection of confidence intervals rule for detecting a possible change in the process generating the data as well as identifying the new data to be used to configure the classifier. A key point of the research is that no assumptions are made about the distribution of the process generating the data. Experimental results show that the proposed adaptive classification system is particularly effective in situations where the process is subject to abrupt changes.
authors: Dipartimento di Elettronica e Informazione, Politecnico di Milano, Milano, Italy. cesare.alippi@polimi.it
authors: Cesare
authors: C
authors: Alippi
authors: Giacomo
authors: G
authors: Boracchi
authors: Manuel
authors: M
authors: Roveri
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016001:Confidence Intervals; D016208:Databases, Factual; D005189:False Positive Reactions; D051188:Knowledge Bases; D016571:Neural Networks, Computer