Improving hyper-parameter self-tuning for data streams by adapting an evolutionary approach

Author:

Moya Antonio R.ORCID,Veloso Bruno,Gama João,Ventura Sebastián

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Computer Science Applications,Information Systems

Reference54 articles.

1. Agrawal R, Imielinski T, Swami A (1993) Database mining: a performance perspective. IEEE Trans Knowl Data Eng 5(6):914–925

2. Bäck T (1996) Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, Oxford

3. Baena-Garcıa M, del Campo-Ávila J, Fidalgo R, et al (2006) Early drift detection method. In: Fourth international workshop on knowledge discovery from data streams, Citeseer, pp 77–86

4. Bahri M, Gomes HM, Bifet A, et al (2020a) Cs-arf: compressed adaptive random forests for evolving data stream classification. In: 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, pp 1–8

5. Bahri M, Maniu S, Bifet A, et al (2020b) Compressed k-nearest neighbors ensembles for evolving data streams. In: ECAI 2020-24th European conference on artificial intelligence

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