@inproceedings{415b858cbf0547d4ba7b8df16d11589b,
title = "An adaptive classification framework for unsupervised model updating in nonstationary environments",
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.",
author = "Piero Conca and Jon Timmis and {De Lemos}, Rog{\'e}rio and Simon Forrest and Heather McCracken",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 1st International Workshop on Machine Learning, Optimization, and Big Data, MOD 2015 ; Conference date: 21-07-2015 Through 23-07-2015",
year = "2015",
doi = "10.1007/978-3-319-27926-8_15",
language = "English",
isbn = "9783319279251",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "171--184",
editor = "Mario Pavone and Farinella, {Giovanni Maria} and Vincenzo Cutello and Panos Pardalos",
booktitle = "Machine Learning, Optimization, and Big Data - 1st International Workshop, MOD 2015 Taormina, Revised Selected Papers",
address = "Switzerland",
}