Počet záznamů: 1
Transfer of Knowledge for Surrogate Model Selection in Cost-Aware Optimization
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SYSNO ASEP 0493290 Druh ASEP A - Abstrakt Zařazení RIV O - Ostatní Název Transfer of Knowledge for Surrogate Model Selection in Cost-Aware Optimization Tvůrce(i) Pitra, Zbyněk (UIVT-O) RID, ORCID, SAI
Repický, Jakub (UIVT-O) ORCID, SAI
Holeňa, Martin (UIVT-O) SAI, RIDZdroj.dok. ECML PKDD 2018: Workshop on Interactive Adaptive Learning. Proceedings. - Dublin, 2018 / Krempl G. ; Lemaire V. ; Kottke D. ; Calma A. ; Holzinger A. ; Polikar R. ; Sick B.
S. 89-94Poč.str. 6 s. Forma vydání Online - E Akce ECML PKDD 2018: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Datum konání 10.09.2018 - 14.09.2018 Místo konání Dublin Země IE - Irsko Typ akce EUR Jazyk dok. eng - angličtina Země vyd. IE - Irsko Klíč. slova Metalearing ; Surrogate model ; Gaussian process ; Random forest ; Exploratory landscape analysis 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 Anotace PUBLISHED IN: ECML PKDD 2018: Workshop on Interactive Adaptive Learning. Proceedings. Dublin, 2018 - (Krempl, G., Lemaire, V., Kottke, D., Calma, A., Holzinger, A., Polikar, R., Sick, B.). s. 89-94. [ECML PKDD 2018: The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. 10.09.2018-14.09.2018, Dublin]. Grant CEP: GA ČR GA17-01251S. ABSTRACT: Surrogate model selection is an active-learning approach to cost-aware continuous black-box optimization in domains where the evaluation of the black-box objective function is expensive, e. g., obtained experimentally or resulting from comprehensive simulations. Active reusing of knowledge represented by landscape properties of the objective function accross different tasks can provide additional information for more reliable decisions in terms of a suitable surrogate model and a suitable setting of its hyperparameters. However, research into using metalearning and especially Exploratory Landscape Analysis (ELA) in this context is only starting. Our goal is to develop a learning system capable to recommend a surrogate model on the basis of the knowledge obtained in previous black-box optimization tasks. In this paper, we provide a first step necessary to construct a learning system applying knowledge from previous tasks to a new one: a study of the applicability of ELA to two important kinds of surrogate models – Gaussian processes (GP) and ensembles of regression trees (random forests, RF). Results using the noiseless benchmarks of the Comparing-Continuous-Optimisers (COCO) platform in the expensive scenario, where at most 50D evaluations are available, are analysed for statistical dependences between model performance and a broad variety of landscape features.
Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2019 Elektronická adresa https://www.ies.uni-kassel.de/p/ial2018/ialatecml2018.pdf
Počet záznamů: 1