Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Conference Object Citation - WoS: 3Citation - Scopus: 13NSEM: Duygu Analizi için Özgün Yıǧınlanmiş Topluluk Yöntemi(Institute of Electrical and Electronics Engineers Inc., 2018-09) Işik, Yunus Emre; Görmez, Yasin; Kaynar, Oǧuz; Aydin, Zafer; Emre Isik, YunusToday, people often share their ideas, opinions and feelings through forums, social media sites, blogs and similar platforms. For this reason, access to these data has become very easy. Increase in the number of shares makes it possible to analyze and use these data in terms of marketing and politics. However, due to the large number of data, it is impossible that this analysis will be done by humans. Determination of what type of emotion is included automatically is done by sentiment analysis methods. In these methods, the text is defined as a mathematical vector and classified by machine learning methods. Ensemble methods are one of the most important methods used as classifiers in sentiment analysis. In these methods, a classifier error is tried to be solved by another classifier. In sentiment analysis, the feature vector that describes the text is as important as the classifier. Feature vectors obtained using different methods can make mistakes in different places. For this reason, in this study, NSEM is proposed for sentiment analysis, which is a new ensemble method that uses 2 different classifiers and 2 different feature extraction methods. As a result of the analysis, the proposed method is the most successful method with an accuracy rate of 79.1%. © 2019 Elsevier B.V., All rights reserved.Conference Object Citation - Scopus: 1Koroner Arter Hastalığı Tanısı İçin Alan Bilgisi İçeren Topluluk Öznitelik Seçim Yöntemi(Institute of Electrical and Electronics Engineers Inc., 2020-10-05) Kolukisa, Burak; Güngör, Vehbi Çağrı; Bakir-Güngör, Burcu; Gungor, Burcu BakirCoronary Artery Disease (CAD) is the condition where, the heart is not fed enough as a result of the accumulation of fatty matter called atheroma in the walls of the arteries. In 2016, CAD accounts for 31% (17.9 million) of the world's total deaths and its diagnosis is difficult. It is estimated that approximately 23.6 million people will die from this disease in 2030. With the development of machine learning and data mining techniques, it might be possible to diagnose CAD inexpensively and easily via examining some physical and biochemical values. In this study, for the CAD classification problem, a novel ensemble feature selection methodology that incorporates domain knowledge is proposed. Via applying the proposed methodology on the UCI Cleveland CAD dataset and using different classification algorithms, performance metrics are compared. It is shown that in our experiments, when Multilayer Perceptron classifier is used with 9 selected features, our proposed solution reached 85.47% accuracy, 82.96% accuracy and 0.839 F-Measure. As a future work, we aim to generate a machine learning model that can quickly diagnose CAD on real-time data in hospitals. © 2021 Elsevier B.V., All rights reserved.