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Genens: An AutoML System for Ensemble Optimization Based on Developmental Genetic Programming
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SYSNO ASEP 0537567 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Genens: An AutoML System for Ensemble Optimization Based on Developmental Genetic Programming Author(s) Suchopárová, Gabriela (UIVT-O) RID, ORCID, SAI
Neruda, Roman (UIVT-O) SAI, RID, ORCIDSource Title 2020 IEEE Symposium Series on Computational Intelligence (SSCI). - New York : IEEE, 2020 - ISBN 978-1-7281-2547-3 Pages s. 631-638 Number of pages 8 s. Publication form Online - E Action IEEE SSCI 2020: IEEE Symposium Series on Computational Intelligence Event date 01.12.2020 - 04.12.2020 VEvent location Canberra / Online Country AU - Australia Event type EUR Language eng - English Country US - United States Keywords Machine learning ; AutoML ; Genetic programming ; Developmental methods ; Pipelines ; Vegetation ; Optimization ; Task analysis ; Machine learning algorithms ; Computational modeling ; Benchmark testing Subject RIV IN - Informatics, Computer Science OECD category Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) R&D Projects GA18-23827S GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 UT WOS 000682772900086 EID SCOPUS 85099713149 DOI 10.1109/SSCI47803.2020.9308582 Annotation We propose an AutoML system for pipeline optimization based on developmental genetic programming — genens. It is built atop of scikit-learn pipelines, and it focuses on both hyperparameter and architecture optimization. Compared to existing systems, it enables to optimize more complex ensembles, while exploring simpler models at the same time. The system has been evaluated on selected benchmark datasets from the AutoML benchmark, producing competitive results. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2021
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