An Adaptive Classification Framework for Unsupervised Model Updating in Nonstationary Environments

Conca, Piero and Timmis, Jon and de Lemos, Rogerio and Forrest, Simon and McCracken, Heather (2015) An Adaptive Classification Framework for Unsupervised Model Updating in Nonstationary Environments. In: International Workshop on Machine learning, Optimization and big Data, July 21 to 23, 2015, Taormina - Sicily, Italy. (Unpublished) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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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.

Item Type: Conference or workshop item (Paper)
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing > Systems Architecture Group
Depositing User: Rogerio de Lemos
Date Deposited: 21 Aug 2015 15:18 UTC
Last Modified: 21 Oct 2015 06:32 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/50274 (The current URI for this page, for reference purposes)
de Lemos, Rogerio: https://orcid.org/0000-0002-0281-6308
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