Feature Selection for Performance Estimation of Machine Learning Workflows
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Feature Selection for Performance Estimation of Machine Learning Workflows
Original language description
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.
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
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
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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Number of pages
9
Pages from-to
351-359
Publisher name
Springer
Place of publication
Cham
Event location
Cusco
Event date
Apr 24, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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