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%2F67985807%3A_____%2F19%3A00512085" target="_blank" >RIV/67985807:_____/19:00512085 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21230/19:00350568
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
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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.n
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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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/GA17-01251S" target="_blank" >GA17-01251S: Metalearning for Extraction of Rules with Numerical Consequents</a><br>
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
ITAT 2019: Information Technologies – Applications and Theory
ISBN
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ISSN
1613-0073
e-ISSN
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Number of pages
7
Pages from-to
51-57
Publisher name
Technical University & CreateSpace Independent Publishing
Place of publication
Aachen
Event location
Donovaly
Event date
Sep 20, 2019
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
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