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This research has been funded by the European Commission's 7th Framework Program, under grant Agreement INSFO-ICT-270428 (iSense).
 

Summary: 1
This research has been funded by the European Commission's 7th
Framework Program, under grant Agreement INSFO-ICT-270428 (iSense).
A just-in-time adaptive classification system based on the intersection of
confidence intervals rule
Cesare Alippi, Giacomo Boracchi, Manuel Roveri
Dipartimento di Elettronica e Informazione
Politecnico di Milano, Milano, Italy
{cesare.alippi, giacomo.boracchi, manuel.roveri}@polimi.it
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.
Keywords: Adaptive Classifiers, Change Detection Tests.
1 Introduction
In the real world, data coming from industrial or environmental processes change their statistical behavior over time due

  

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

 

Collections: Computer Technologies and Information Sciences