Ghomeshi, Hossein;
Gaber, Mohamed Medhat;
Kovalchuk, Yevgeniya;
(2019)
EACD: evolutionary adaptation to concept drifts in data streams.
Data Mining and Knowledge Discovery
, 33
pp. 663-694.
10.1007/s10618-019-00614-6.
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Abstract
This paper presents a novel ensemble learning method based on evolutionary algorithms to cope with different types of concept drifts in non-stationary data stream classification tasks. In ensemble learning, multiple learners forming an ensemble are trained to obtain a better predictive performance compared to that of a single learner, especially in non-stationary environments, where data evolve over time. The evolution of data streams can be viewed as a problem of changing environment, and evolutionary algorithms offer a natural solution to this problem. The method proposed in this paper uses random subspaces of features from a pool of features to create different classification types in the ensemble. Each such type consists of a limited number of classifiers (decision trees) that have been built at different times over the data stream. An evolutionary algorithm (replicator dynamics) is used to adapt to different concept drifts; it allows the types with a higher performance to increase and those with a lower performance to decrease in size. Genetic algorithm is then applied to build a two-layer architecture based on the proposed technique to dynamically optimise the combination of features in each type to achieve a better adaptation to new concepts. The proposed method, called EACD, offers both implicit and explicit mechanisms to deal with concept drifts. A set of experiments employing four artificial and five real-world data streams is conducted to compare its performance with that of the state-of-the-art algorithms using the immediate and delayed prequential evaluation methods. The results demonstrate favourable performance of the proposed EACD method in different environments.
| Type: | Article |
|---|---|
| Title: | EACD: evolutionary adaptation to concept drifts in data streams |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s10618-019-00614-6 |
| Publisher version: | https://doi.org/10.1007/s10618-019-00614-6 |
| Language: | English |
| Additional information: | © The Author(s), 2019. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Data streams, Ensemble learning, Concept drifts, Evolutionary algorithms, Genetic algorithm, Non-stationary environments |
| UCL classification: | UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10177762 |
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