Počet záznamů: 1
Sequential model building in symbolic regression
- 1.
SYSNO ASEP 0512085 Druh ASEP C - Konferenční příspěvek (mezinárodní konf.) Zařazení RIV D - Článek ve sborníku Název Sequential model building in symbolic regression Tvůrce(i) Žegklitz, Jan (UIVT-O)
Pošík, M. (CZ)Zdroj.dok. 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 Rozsah stran s. 51-57 Poč.str. 7 s. Forma vydání Online - E Akce ITAT 2019: Conference Information Technologies - Applications and Theory /19./ Datum konání 20.09.2019 - 24.09.2019 Místo konání Donovaly Země SK - Slovensko Typ akce EUR Jazyk dok. eng - angličtina Země vyd. DE - Německo Klíč. slova genetic programming ; symbolic regression ; boosting ; sequential learning Vědní obor RIV IN - Informatika Obor OECD Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) CEP GA17-01251S GA ČR - Grantová agentura ČR Institucionální podpora UIVT-O - RVO:67985807 EID SCOPUS 85074095603 Anotace 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.
Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2020 Elektronická adresa http://ceur-ws.org/Vol-2473/paper5.pdf
Počet záznamů: 1