RT Journal Article
T1 DRED: 												An evolutionary diversity generation method for concept drift adaptation in online learning environments
A1 L. Lobo, Jesus
A1 Del Ser, Javier
A1 Bilbao, Miren Nekane
A1 Perfecto, Cristina
A1 Salcedo-Sanz, Sancho
AB Nowadays fast-arriving information flows lay the basis of many data mining applications. Such data streams are usually affected by non-stationary events that eventually change their distribution (concept drift), causing that predictive models trained over these data become obsolete and do not adapt suitably to the new distribution. Specially in online learning scenarios, there is a pressing need for new algorithms that adapt to this change as fast as possible, while maintaining good performance scores. Recent studies have revealed that a good strategy is to construct highly diverse ensembles towards utilizing them shortly after the drift (independently from the type of drift) to obtain good performance scores. However, the existence of the so-called trade-off between stability (performance over stable data concepts) and plasticity (recovery and adaptation after drift events) implies that the construction of the ensemble model should account simultaneously for these two conflicting objectives. In this regard, this work presents a new approach to artificially generate an optimal diversity level when building prediction ensembles once shortly after a drift occurs. The approach uses a Kernel Density Estimation (KDE) method to generate synthetic data, which are subsequently labeled by means a multi-objective optimization method that allows training each model of the ensemble with a different subset of synthetic samples. Computational experiments reveal that the proposed approach can be hybridized with other traditional diversity generation approaches, yielding optimized levels of diversity that render an enhanced recovery from drifts.
SN 1568-4946
YR 2018
FD 2018-07
LK https://hdl.handle.net/11556/3679
UL https://hdl.handle.net/11556/3679
LA eng
NO L. Lobo , J , Del Ser , J , Bilbao , M N , Perfecto , C & Salcedo-Sanz , S 2018 , ' DRED : An evolutionary diversity generation method for concept drift adaptation in online learning environments ' , Applied Soft Computing Journal , vol. 68 , pp. 693-709 . https://doi.org/10.1016/j.asoc.2017.10.004
NO Publisher Copyright: © 2017 Elsevier B.V.
NO This work was supported by the Basque Government through the ELKARTEK program (ref. KK-2016/00096, BID3ABI project). The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper.
DS TECNALIA Publications
RD 29 jul 2024