Greedy-AutoML: A novel greedy-based stacking ensemble learning framework for assessing soil liquefaction potential

Basit Öğe Kaydı
dc.authorid0000-0002-9830-8585en_US
dc.authorid0000-0003-2520-4395en_US
dc.contributor.authorŞahin, Emrehan Kutluğ
dc.contributor.authorDemir, Selçuk
dc.date.accessioned2023-11-15T08:25:41Z
dc.date.available2023-11-15T08:25:41Z
dc.date.issued2023en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractAutomated machine learning (AutoML) is a generic term for a specific approach to machine learning (ML) area that tries to automate the end-to-end process of employing repetitive ML tasks for real-world problems. In recent years, the AutoML framework, which is the subject of an increasing number of research articles, has become a potential approach for developing complicated ML models without human experience and support. Although existing techniques on AutoML have yielded promising results, research in this field is immature, and new approaches should be developed gradually. This study describes a novel AutoML framework to predict soil liquefaction potential problem based on stacking ensemble learning (SEL) combined with a greedy search algorithm. A special AutoML framework, called Greedy-AutoML, is presented that automatically produces an optimized ML model for predicting on a supervised classification task. The general concept of the proposed AutoML framework consists of three main steps: data preparation, greedy feature selection, and greedy stacking ensemble. Furthermore, the Greedy-AutoML framework has been published on a user-friendly web-based platform for testing or trial purposes. To highlight the capability of this AutoML application, Greedy-AutoML is applied to predict the liquefaction potential of soils using three well-known datasets (i.e., CPT - cone penetration test, SPT - standard penetration test, and Vs - shear wave velocity test) collected from previously published research. The results are assessed based on different performance matrices, namely Accuracy (Acc), Kappa, Precision, Recall, and F1-Score. Experiments with datasets from existing case histories with varying distribution and features showed that the proposed greedy based SEL method achieved an Acc of 98% for the CPT and SPT datasets, while the achieved Acc for the Vs dataset was about 99%.en_US
dc.identifier.citationSahin, E. K., & Demir, S. (2023). Greedy-AutoML: A novel greedy-based stacking ensemble learning framework for assessing soil liquefaction potential. Engineering Applications of Artificial Intelligence, 119, 105732.en_US
dc.identifier.doi10.1016/j.engappai.2022.105732
dc.identifier.endpage17en_US
dc.identifier.issn0952-1976
dc.identifier.issn1873-6769
dc.identifier.scopus2-s2.0-85145649620en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.engappai.2022.105732
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11834
dc.identifier.volume119en_US
dc.identifier.wosWOS:000909711400001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorŞahin, Emrehan Kutluğ
dc.institutionauthorDemir, Selçuk
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofEngineering Applications of Artificial Intelligenceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAutoMLen_US
dc.subjectGreedy Searching Algorithmen_US
dc.subjectFeature Selectionen_US
dc.subjectSoil Liquefactionen_US
dc.subjectMachineen_US
dc.subjectAlgorithmen_US
dc.titleGreedy-AutoML: A novel greedy-based stacking ensemble learning framework for assessing soil liquefaction potentialen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
emrehan-kutlug-sahin.pdf
Boyut:
3.96 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin/Full Text
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
İnşaat Mühendisliği Bölümü Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu