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Computer assisted automatic diagnostic systems are used for the purpose of speeding up diagnosis and treatment and helping to make the right decision. In this study, cervical cancer is identified using four basic classifiers: Naive Bayes (NB), k-Nearest Neighbor (kNN), Multilayer Perceptron (MLP) and Decision Trees (KA-C4.5) algorithms and random subspaces ensemble algorithm. Gain Ratio Attribute Evaluation (GRAE) feature extraction algorithm is applied to contribute to classification performance. The classification results obtained with all datasets and reduced datasets are compared with respect to performance criteria such as accuracy, Root Mean Square Error (RMSE), Sensitivity, Specificity performance criteria. According to the obtained performance analysis, it is seen that the classification performance with the random subspace ensemble algorithm using the kNN basic classifier on the reduced data set is the highest (%95.51). © 2018 IEEE.
| Yazar |
Erkaymaz, Okan Palabaş, Tuğba |
| Yayın Türü | Proceedings |
| Tek Biçim Adres | https://hdl.handle.net/20.500.12628/4685 |
| Konu Başlıkları |
Basic classifiers
Cervical cancer Feature extraction Random subspaces |
| Koleksiyonlar |
Fakülteler Mühendislik Fakültesi Bilgisayar Mühendisliği Bölümü Bildiri Koleksiyonu (Bilgisayar Mühendisliği Bölümü) Fakülteler Mühendislik Fakültesi Biyomedikal Mühendisliği Bölümü Bildiri Koleksiyonu (Biyomedikal Mühendisliği Bölümü) Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed | SOBİAD Scopus İndeksli Yayınlar Koleksiyonu |
| Dergi Adı | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 |
| Sayfalar | 1 - 4 |
| Yayın Yılı | 2018 |
| Eser Adı [dc.title] | Classification of cervical cancer data and the effect of random subspace algorithms on classification performance |
| Yazar [dc.contributor.author] | Erkaymaz, Okan |
| Yazar [dc.contributor.author] | Palabaş, Tuğba |
| Yayın Yılı [dc.date.issued] | 2018 |
| Yayıncı [dc.publisher] | Institute of Electrical and Electronics Engineers Inc. |
| Yayın Türü [dc.type] | proceedings |
| Açıklama [dc.description] | Aselsan;et al.;Huawei;IEEE Signal Processing Society;IEEE Turkey Section;Netas |
| Açıklama [dc.description] | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 -- 2 May 2018 through 5 May 2018 -- -- 137780 |
| Özet [dc.description.abstract] | Computer assisted automatic diagnostic systems are used for the purpose of speeding up diagnosis and treatment and helping to make the right decision. In this study, cervical cancer is identified using four basic classifiers: Naive Bayes (NB), k-Nearest Neighbor (kNN), Multilayer Perceptron (MLP) and Decision Trees (KA-C4.5) algorithms and random subspaces ensemble algorithm. Gain Ratio Attribute Evaluation (GRAE) feature extraction algorithm is applied to contribute to classification performance. The classification results obtained with all datasets and reduced datasets are compared with respect to performance criteria such as accuracy, Root Mean Square Error (RMSE), Sensitivity, Specificity performance criteria. According to the obtained performance analysis, it is seen that the classification performance with the random subspace ensemble algorithm using the kNN basic classifier on the reduced data set is the highest (%95.51). © 2018 IEEE. |
| Kayıt Giriş Tarihi [dc.date.accessioned] | 2019-12-23 |
| Açık Erişim Tarihi [dc.date.available] | 2019-12-23 |
| Yayın Dili [dc.language.iso] | tur |
| Konu Başlıkları [dc.subject] | Basic classifiers |
| Konu Başlıkları [dc.subject] | Cervical cancer |
| Konu Başlıkları [dc.subject] | Feature extraction |
| Konu Başlıkları [dc.subject] | Random subspaces |
| Haklar [dc.rights] | info:eu-repo/semantics/closedAccess |
| Alternatif Başlık [dc.title.alternative] | Serviks kanseri verilerinin sınıflandırılması ve rastsal altuzaylar algoritmasının sınıflandırma performansına etkisi |
| İlk Sayfa Sayısı [dc.identifier.startpage] | 1 |
| Son Sayfa Sayısı [dc.identifier.endpage] | 4 |
| Dergi Adı [dc.relation.journal] | 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 |
| Tek Biçim Adres [dc.identifier.uri] | https://dx.doi.org/10.1109/SIU.2018.8404197 |
| Tek Biçim Adres [dc.identifier.uri] | https://hdl.handle.net/20.500.12628/4685 |