1. bookTom 27 (2022): Zeszyt 1 (June 2022)
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eISSN
2255-8691
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08 Nov 2012
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Incorporating Feature Selection Methods into Machine Learning-Based Covid-19 Diagnosis

Data publikacji: 23 Aug 2022
Tom & Zeszyt: Tom 27 (2022) - Zeszyt 1 (June 2022)
Zakres stron: 13 - 18
Informacje o czasopiśmie
License
Format
Czasopismo
eISSN
2255-8691
Pierwsze wydanie
08 Nov 2012
Częstotliwość wydawania
2 razy w roku
Języki
Angielski

[1] A. Til, “Yeni Koranavirüs hastalığı hakkında bilinmesi gerekenler”, in 2020 Göller Bölgesi Aylık Ekonomi ve Kültür Dergisi, 2020, pp. 53–57. Search in Google Scholar

[2] D. M. Ali, L. G. Zake, & N. K. El Kady, “Role of chest computed tomography versus real time reverse transcription polymerase chain reaction for diagnosis of COVID-19: A systematic review and meta-analysis”, Interdisciplinary Perspectives on Infectious Diseases, vol. 2021, Jun. 2021, Art no. 8798575. https://doi.org/10.1155/2021/8798575818432234194491 Search in Google Scholar

[3] U. Bilge, “Tıpta yapay zekâ ve uzman sistemler”, Türkiye Bilişim Derneği Kongresi, 2007, pp. 113–118. Search in Google Scholar

[4] L. Li et al., “Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT”, Radiology, vol. 296, no. 2, Mar. 2020, Art no. 200905. https://doi.org/10.1148/radiol.2020200905723347332191588 Search in Google Scholar

[5] F. S. H. De Souza, N. S. Hojo-Souza, E. B. Dos Santos, C. M. Da Silva, & D. L. Guidoni, “Predicting the disease outcome in COVID-19 positive patients through machine learning: A retrospective cohort study with Brazilian data”, Frontiers in Artificial Intelligence, vol. 4, 2021, Art no. 579931. https://doi.org/10.3389/frai.2021.579931842786734514377 Search in Google Scholar

[6] H. Budak, “Özellik seçim yöntemleri ve yeni bir yaklaşım”, Journal of the Institute of Natural and Applied Sciences of Süleyman Demirel University, vol. 22(Private), 21, 2018.10.19113/sdufbed.01653 Search in Google Scholar

[7] M. Karakaş. “Sınıflandırma problemlerinde özellik seçimi için karşıtlık tabanlı gri kurt optimizasyon algoritması”, Master Thesis, Bilecik Şeyh Edebali University, 2020, pp. 80. Search in Google Scholar

[8] W. Yang, K. Wang, & W. Zuo, “Neighborhood component feature selection for high-dimensional data”, JCP, vol. 7, pp. 161–168, 2012. https://doi.org/10.4304/jcp.7.1.161-168 Search in Google Scholar

[9] Neighborhood component analysis (NCA) feature selection – MATLAB & Simulink. [Online]. Available: https://www.mathworks.com/help/stats/neighborhood-component-analysis.html. Accessed on: November 24, 2021. Search in Google Scholar

[10] K. Güçkıran, İ. Cantürk, & L. Özyılmaz, “DNA microarray gene expression data classification using SVM, MLP, and RF with feature selection methods relief and LASSO”, Journal of the Institute of Natural and Applied Sciences of Suleyman Demirel University, vol. 23, no. 1, pp. 115–121, Apr. 2019. https://doi.org/10.19113/sdufenbed.453462 Search in Google Scholar

[11] T. Miftahushudur, C. Wael, & T. Praludi, “Infinite latent feature selection technique for hyperspectral image classification”, Jurnal Elektronika dan Telekomunikasi, vol. 19, no. 1, pp. 32–37, 2019. https://doi.org/10.14203/jet.v19.32-37 Search in Google Scholar

[12] F. Haider, S. Pollak, P. Albert, & S. Luz, “Emotion recognition in low-resource settings: An evaluation of automatic feature selection methods”, Computer Speech & Language, vol. 65, Jan. 2021, Art no. 101119. https://doi.org/10.1016/j.csl.2020.101119 Search in Google Scholar

[13] G. Cong, W.-C. Peng, W. E. Zhang, C. Li, & A. Sun, “Advanced data mining and applications”, in 13th International Conference, ADMA 2017, Singapore, November 5–6, 2017. https://doi.org/10.1007/978-3-319-69179-4 Search in Google Scholar

[14] G. Roffo, S. Melzi, U. Castellani, A. Vinciarelli, & M. Cristani, “Infinite feature selection: A graph-based feature filtering approach”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 12, pp. 4396–4410, Dec. 2021. https://doi.org/10.1109/TPAMI.2020.300284332750789 Search in Google Scholar

[15] Infinite Feature Selection – File Exchange – MATLAB Central. [Online]. Available: https://www.mathworks.com/matlabcentral/fileexchange/54763-infinite-feature-selection. Accessed on: November 24, 2021. Search in Google Scholar

[16] S. Adams, R. Meekins, & P. A. Beling, “An empirical evaluation of techniques for feature selection with cost”, in 2017 IEEE International Conference on Data Mining Workshops (ICDMW), New Orleans, LA, USA, 2017, pp.834–841. https://doi.org/10.1109/ICDMW.2017.153 Search in Google Scholar

[17] F. Liu, & X. Liu, “Unsupervised feature selection for multi-cluster data via smooth distributed score”, in Emerging Intelligent Computing Technology and Applications, D.-S. Içinde, P. Huang, P. Gupta, X. Zhang, & P. Premaratne, Eds. Springer, Berlin, Heidelberg, 2012, pp. 74–79. https://doi.org/10.1007/978-3-642-31837-5_11 Search in Google Scholar

[18] R. Duangsoithong, & T. Windeatt, “Correlation-based and causal feature selection analysis for ensemble classifiers”, in Artificial Neural Networks in Pattern Recognition, F. Schwenker, & N. El Gayar, Eds. Springer, Berlin, Heidelberg, 2010, pp. 25–36. https://doi.org/10.1007/978-3-642-12159-3_3 Search in Google Scholar

[19] E. Yakut, B. Elmas, & S. Yavuz, “Yapay sinir ağları ve destek vektör makineleri yöntemleri borsa endeksi tahmini“, Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, vol. 19, pp. 139–157, 2014. Search in Google Scholar

[20] T. Kavzoğlu, & İ. Colkesen, “Karar ağaçları İle uydu görüntülerinin sınıflandırılması: Kocaeli Örneği“, Harita Teknolojileri Elektronik Dergisi, vol. 2, no. 1, pp. 36–45, 2010. Search in Google Scholar

[21] M. F. Akca, “Karar Ağaçları (Makine Öğrenmesi Serisi-3)“, Deep Learning Türkiye. [Online]. Available: https://medium.com/deep-learning-turkiye/karar-a%C4%9Fa%C3%A7lar%C4%B1-makine-%C3%B6%C4%9Frenmesi-serisi-3-a03f3ff00ba5. Accessed on: November 23, 2020). Search in Google Scholar

[22] E. Arslan, “Makine Öğrenmesi – KNN (K-Nearest neighbors) algoritması”, Medium. [Online]. Available: https://medium.com/@arslanev/makine-%C3%B6%C4%9Frenmesiknn-k-nearest-neighbors-algoritmas%C4%B1-bdfb688d7c5f. Accessed on: May 19, 2020. Search in Google Scholar

[23] E. Çelik, M. Atalay, & H. Bayer, “Yapay sinir ağları ve destek vektör makineleri ile deprem tahminde sismik darbelerin kullanılması”, in 2014 IEEE 22nd Signal Processing and Communications Applications Conference, 2014. Search in Google Scholar

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