TY - GEN
T1 - An adaptive classification framework for unsupervised model updating in nonstationary environments
AU - Conca, Piero
AU - Timmis, Jon
AU - De Lemos, Rogério
AU - Forrest, Simon
AU - McCracken, Heather
PY - 2015/1/1
Y1 - 2015/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84955277864&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-27926-8_15
DO - 10.1007/978-3-319-27926-8_15
M3 - Conference contribution
AN - SCOPUS:84955277864
SN - 9783319279251
VL - 9432
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 171
EP - 184
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer
T2 - 1st International Workshop on Machine Learning, Optimization, and Big Data, MOD 2015
Y2 - 21 July 2015 through 23 July 2015
ER -