dc.contributor.author | Aydin Z. | |
dc.contributor.author | Kaynar O. | |
dc.contributor.author | Görmez Y. | |
dc.contributor.author | Işik Y.E. | |
dc.date.accessioned | 2019-07-27T12:10:23Z | |
dc.date.accessioned | 2019-07-28T09:33:10Z | |
dc.date.available | 2019-07-27T12:10:23Z | |
dc.date.available | 2019-07-28T09:33:10Z | |
dc.date.issued | 2018 | |
dc.identifier.isbn | 9781538615010 | |
dc.identifier.uri | https://dx.doi.org/10.1109/SIU.2018.8404547 | |
dc.identifier.uri | https://hdl.handle.net/20.500.12418/5700 | |
dc.description | Aselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netas | en_US |
dc.description | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- | en_US |
dc.description.abstract | Three-dimensional structure prediction is one of the important problems in bioinformatics and theoretical chemistry. One of the most important steps in the three-dimensional structure prediction is the estimation of secondary structure. Due to rapidly growing databases and recent feature extraction methods datasets used for predicting secondary structure can potentially contain a large number of samples and dimensions. For this reason, it is important to use algorithms that are fast and accurate. In this study, various classification algorithms have been optimized for the second phase of a two-stage classifier on EVAset benchmark both in the original input space and in the space reduced using the information gain metric. The most accurate classifier is obtained as the support vector machine while the extreme learning machine is significantly faster in model training. © 2018 IEEE. | en_US |
dc.language.iso | tur | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
dc.relation.isversionof | 10.1109/SIU.2018.8404547 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Feature Selection | en_US |
dc.subject | Machine Learning | en_US |
dc.subject | Protein Structure Prediction | en_US |
dc.subject | Secondary Structure Prediction | en_US |
dc.title | Comparison of machine learning classifiers for protein secondary structure prediction [Protein ikincil yapi tahmini için makine ö?renmesi yöntemlerinin karşilaştirilmasi] | en_US |
dc.type | conferenceObject | en_US |
dc.relation.journal | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 | en_US |
dc.contributor.department | Aydin, Z., Bilgisayar Mühendisli?i, Abdullah Gül Üniversitesi, Kayseri, Turkey -- Kaynar, O., Yönetim Bilişim Sistemleri, Cumhuriyet Üniversitesi, Sivas, Turkey -- Görmez, Y., Yönetim Bilişim Sistemleri, Cumhuriyet Üniversitesi, Sivas, Turkey -- Işik, Y.E., Yönetim Bilişim Sistemleri, Cumhuriyet Üniversitesi, Sivas, Turkey | en_US |
dc.identifier.endpage | 4 | en_US |
dc.identifier.startpage | 1 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |