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Sequential model building in symbolic regression

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00350568" target="_blank" >RIV/68407700:21230/19:00350568 - isvavai.cz</a>

  • Alternative codes found

    RIV/67985807:_____/19:00512085

  • Result on the web

    <a href="http://ceur-ws.org/Vol-2473/paper5.pdf" target="_blank" >http://ceur-ws.org/Vol-2473/paper5.pdf</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Sequential model building in symbolic regression

  • Original language description

    Symbolic Regression is a supervised learning technique for regression based on Genetic Programming. A popular algorithm is the Multi-Gene Genetic Programming which builds models as a linear combination of a number of components which are all built together. However, in recent years a different approach emerged, represented by the Sequential Symbolic Regression algorithm, which builds the model sequentially, one component at a time, and the components are combined using a method based on geometric semantic crossover. In this article we show that the SSR algorithm effectively produces linear combination of components and we introduce another sequential approach very similar to classical ensemble method of boosting. All algorithms are compared with MGGP as a baseline on a number of real-world datasets. The results show that the sequential approaches are overall worse than MGGP both in terms of accuracy and model size.

  • 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

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2019

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Proceedings of the 19th Conference Information Technologies - Applications and Theory (ITAT 2019)

  • ISBN

  • ISSN

    1613-0073

  • e-ISSN

  • Number of pages

    7

  • Pages from-to

    51-57

  • Publisher name

    CEUR Workshop Proceedings

  • Place of publication

    Aachen

  • Event location

    Donovaly

  • Event date

    Sep 20, 2019

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article