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Sequential model building in symbolic regression
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SYSNO ASEP 0512085 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Sequential model building in symbolic regression Author(s) Žegklitz, Jan (UIVT-O)
Pošík, M. (CZ)Source Title ITAT 2019: Information Technologies – Applications and Theory. - Aachen : Technical University & CreateSpace Independent Publishing, 2019 / Barančíková P. ; Holeňa M. ; Horváth T. ; Pleva M. ; Rosa R. - ISSN 1613-0073 Pages s. 51-57 Number of pages 7 s. Publication form Online - E Action ITAT 2019: Conference Information Technologies - Applications and Theory /19./ Event date 20.09.2019 - 24.09.2019 VEvent location Donovaly Country SK - Slovakia Event type EUR Language eng - English Country DE - Germany Keywords genetic programming ; symbolic regression ; boosting ; sequential learning 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 GA17-01251S GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 EID SCOPUS 85074095603 Annotation 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.
Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2020 Electronic address http://ceur-ws.org/Vol-2473/paper5.pdf
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