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2015 | OriginalPaper | Buchkapitel

An Adaptive Classification Framework for Unsupervised Model Updating in Nonstationary Environments

verfasst von : Piero Conca, Jon Timmis, Rogério de Lemos, Simon Forrest, Heather McCracken

Erschienen in: Machine Learning, Optimization, and Big Data

Verlag: Springer International Publishing

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Abstract

This paper introduces an adaptive framework that makes use of ensemble classification and self-training to maintain high classification performance in datasets affected by concept drift without the aid of external supervision to update the model of a classifier. The updating of the model of the framework is triggered by a mechanism that infers the presence of concept drift based on the analysis of the differences between the outputs of the different classifiers. In order to evaluate the performance of the proposed algorithm, comparisons were made with a set of unsupervised classification techniques and drift detection techniques. The results show that the framework is able to react more promptly to performance degradation than the existing methods and this leads to increased classification accuracy. In addition, the framework stores a smaller amount of instances with respect to a single-classifier approach.

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Metadaten
Titel
An Adaptive Classification Framework for Unsupervised Model Updating in Nonstationary Environments
verfasst von
Piero Conca
Jon Timmis
Rogério de Lemos
Simon Forrest
Heather McCracken
Copyright-Jahr
2015
DOI
https://doi.org/10.1007/978-3-319-27926-8_15