Feature Extraction Based on Deep Learning for Some Traditional Machine Learning Methods

dc.contributor.author Çayır, Aykut
dc.contributor.author Dağ, Hasan
dc.contributor.author Yenidoğan, Işıl
dc.contributor.author Yenidoğan Dağ, Işıl
dc.contributor.author Dağ, Hasan
dc.contributor.other Management Information Systems
dc.date.accessioned 2021-01-09T11:57:37Z
dc.date.available 2021-01-09T11:57:37Z
dc.date.issued 2018
dc.department Fakülteler, İşletme Fakültesi, Yönetim Bilişim Sistemleri Bölümü en_US
dc.description.abstract Deep learning is a subfield of machine learning and deep neural architectures can extract high level features automatically without handcraft feature engineering unlike traditional machine learning algorithms. In this paper, we propose a method, which combines feature extraction layers of a convolutional neural network with traditional machine learning algorithms, such as, support vector machine, gradient boosting machines, and random forest. All of the proposed hybrid models and the above mentioned machine learning algorithms are trained on three different datasets: MNIST, Fashion-MNIST, and CIFAR-10. Results show that the proposed hybrid models are more successful than traditional models while they are being trained from raw pixel values. In this study, we empower traditional machine learning algorithms for classification using feature extraction ability of deep neural network architectures and we are inspired by transfer learning methodology to this. en_US
dc.identifier.citationcount 26
dc.identifier.doi 10.1109/UBMK.2018.8566383 en_US
dc.identifier.isbn 978-153867893-0
dc.identifier.scopus 2-s2.0-85060661178 en_US
dc.identifier.uri https://hdl.handle.net/20.500.12469/3719
dc.identifier.uri https://doi.org/10.1109/UBMK.2018.8566383
dc.identifier.wos WOS:000459847400095 en_US
dc.institutionauthor Çayır, Aykut en_US
dc.institutionauthor Yenidoğan, Işıl en_US
dc.institutionauthor Daǧ, Hasan en_US
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation.journal UBMK 2018 - 3rd International Conference on Computer Science and Engineering en_US
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 47
dc.title Feature Extraction Based on Deep Learning for Some Traditional Machine Learning Methods en_US
dc.type Conference Object en_US
dc.wos.citedbyCount 36
dspace.entity.type Publication
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