Sequential model building in symbolic regression

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F19%3A00512085" target="_blank" >RIV/67985807:_____/19:00512085 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21230/19:00350568

Výsledek na webu

<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

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Jazyk výsledku

angličtina

Název v původním jazyce

Sequential model building in symbolic regression

Popis výsledku v původním jazyce

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.n

Název v anglickém jazyce

Sequential model building in symbolic regression

Popis výsledku anglicky

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.n

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

<a href="/cs/project/GA17-01251S" target="_blank" >GA17-01251S: Metaučení pro extrakci pravidel s numerickými konsekventy</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

ITAT 2019: Information Technologies – Applications and Theory

ISBN

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ISSN

1613-0073

e-ISSN

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Počet stran výsledku

7

Strana od-do

51-57

Název nakladatele

Technical University & CreateSpace Independent Publishing

Místo vydání

Aachen

Místo konání akce

Donovaly

Datum konání akce

20. 9. 2019

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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