Categorization of the models based on structural information extraction and machine learning

Basit Öğe Kaydı
dc.authorid0000-0002-0397-6282
dc.authorid0000-0002-5898-7464
dc.authorid0000-0002-5436-6070
dc.contributor.authorKhalilipour, Alirezaen_US
dc.contributor.authorBozyiğit, Fatmaen_US
dc.contributor.authorUtku, Canen_US
dc.contributor.authorChallenger, Moharramen_US
dc.date.accessioned2022-08-31T07:18:44Z
dc.date.available2022-08-31T07:18:44Z
dc.date.issued2022-07-21
dc.departmentIşık Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering and Natural Sciences, Department of Computer Engineeringen_US
dc.description.abstractAs various engineering fields increasingly use modelling techniques, the number of provided models, their size, and their structural complexity increase. This makes model management, including finding these models, with state of the art very expensive computationally, i.e., leads to non-tractable graph comparison algorithms. To handle this problem, modelers can organize available models to be reused and overcome the development of the new and more complex models with less cost and effort. Therefore, we utilized a model classification using baseline machine learning approaches on a dataset including 555 Ecore metamodels. In our proposed system, the structural information of each model was summarized in its elements through generating their simple labelled graphs. The proposed solution is to transform the complex attributed graphs of the models to simply labelled graphs so that graph analysis algorithms can be applied to them. The labelled graphs (models) were structurally compared using graph comparison techniques such as graph kernels, and the results were used as a set of features for similarity search. After generating feature vectors, the performance of six machine learning classifiers (Naïve Bayes (NB), k Nearest Neighbors (kNN), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) were evaluated on the feature vectors. The presented model yields promising results for the model classification task with a classification accuracy over 87%.en_US
dc.description.versionPublisher's Versionen_US
dc.identifier.citationKhalilipour, A., Bozyiğit, F., Utku, C. & Challenger, M. (2022). Categorization of the models based on structural information extraction and machine learning. Paper presented at the Lecture Notes in Networks and Systems, Volume 505 LNNS, 173-181. doi:10.1007/978-3-031-09176-6_21en_US
dc.identifier.doi10.1007/978-3-031-09176-6_21
dc.identifier.endpage181
dc.identifier.isbn9783031091759
dc.identifier.isbn9783031091766
dc.identifier.issn2367-3370
dc.identifier.issn2367-3389
dc.identifier.scopus2-s2.0-85135096558
dc.identifier.scopusqualityQ4
dc.identifier.startpage173
dc.identifier.urihttps://hdl.handle.net/11729/4803
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-031-09176-6_21
dc.identifier.volume505
dc.identifier.wosWOS:000889132600021
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakConference Proceedings Citation Index – Science (CPCI-S)en_US
dc.institutionauthorUtku, Canen_US
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofLecture Notes in Networks and Systemsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectGraph Kernel methodsen_US
dc.subjectMachine learning methodsen_US
dc.subjectModel managementen_US
dc.subjectModel transformationen_US
dc.subjectModel-driven engineeringen_US
dc.subjectGraph grammarsen_US
dc.titleCategorization of the models based on structural information extraction and machine learningen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
Categorization_of_the_Models_Based_on_Structural_Information_Extraction_and_Machine_Learning.pdf
Boyut:
370.15 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Publisher's Version
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

Bildiri Koleksiyonu | Bilgisayar Mühendisliği Bölümü
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu