Feature Selection for Performance Estimation of Machine Learning Workflows

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F23%3A00579589" target="_blank" >RIV/67985807:_____/23:00579589 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-33258-6_33" target="_blank" >https://doi.org/10.1007/978-3-031-33258-6_33</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-33258-6_33" target="_blank" >10.1007/978-3-031-33258-6_33</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Feature Selection for Performance Estimation of Machine Learning Workflows

Popis výsledku v původním jazyce

Performance prediction of machine learning models can speed up automated machine learning procedures and it can be also incorporated into model recommendation algorithms. We propose a meta-learning framework that utilizes information about previous runs of machine learning workflows on benchmark tasks. We extract features describing the workflows and meta-data about tasks, and combine them to train a regressor for performance prediction. This way, we obtain the model performance prediction without any training, just by means of feature extraction and inference via the regressor. The approach is tested on OpenML-CC18 Curated Classification benchmark estimating the 75th percentile value of area under the ROC curve (AUC) of the classifiers. We were able to obtain consistent predictions with $$R^2$$ score of 0.8 for previously unseen data.

Název v anglickém jazyce

Feature Selection for Performance Estimation of Machine Learning Workflows

Popis výsledku anglicky

Performance prediction of machine learning models can speed up automated machine learning procedures and it can be also incorporated into model recommendation algorithms. We propose a meta-learning framework that utilizes information about previous runs of machine learning workflows on benchmark tasks. We extract features describing the workflows and meta-data about tasks, and combine them to train a regressor for performance prediction. This way, we obtain the model performance prediction without any training, just by means of feature extraction and inference via the regressor. The approach is tested on OpenML-CC18 Curated Classification benchmark estimating the 75th percentile value of area under the ROC curve (AUC) of the classifiers. We were able to obtain consistent predictions with $$R^2$$ score of 0.8 for previously unseen data.

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

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

International Conference on Information Technology and Systems: ICITS 2023, Volume 1

ISBN

978-3-031-33257-9

ISSN

2367-3370

e-ISSN

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Počet stran výsledku

9

Strana od-do

351-359

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Cusco

Datum konání akce

24. 4. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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