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Genens: An AutoML System for Ensemble Optimization Based on Developmental Genetic Programming

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00537567" target="_blank" >RIV/67985807:_____/20:00537567 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1109/SSCI47803.2020.9308582" target="_blank" >http://dx.doi.org/10.1109/SSCI47803.2020.9308582</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/SSCI47803.2020.9308582" target="_blank" >10.1109/SSCI47803.2020.9308582</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Genens: An AutoML System for Ensemble Optimization Based on Developmental Genetic Programming

  • Original language description

    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.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

    <a href="/en/project/GA18-23827S" target="_blank" >GA18-23827S: Capabilities and limitations of shallow and deep networks</a><br>

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2020

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    2020 IEEE Symposium Series on Computational Intelligence (SSCI)

  • ISBN

    978-1-7281-2548-0

  • ISSN

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    631-638

  • Publisher name

    IEEE

  • Place of publication

    New York

  • Event location

    Canberra / Online

  • Event date

    Dec 1, 2020

  • Type of event by nationality

    EUR - Evropská akce

  • UT code for WoS article