Research Article
Year 2019,
Volume: 2 Issue: 1
,
30
-
34
,
13.07.2019
Abstract
Günümüzde mikrodizi analizlerinden
kanser teşhisi önemli bir araştırmadır. Bireysel genlerden elde edilen mikro
dizi verisinde kanser teşhisi için makine öğrenmesi yöntemlerini kullanmanın,
zaman ve doğruluk açısından avantajları vardır.Akciğer ve beyin kanseri veri
setleri üzerinde makine öğrenmesi sınıflandırma yöntemleri kullanılarak
performans analizi yapıldı. Aynı veriler genetik algoritma ile öznitelik
seçimine tabii tutuldu ve öznitelik seçimi yapılmış verilerin performans
analizleri tekrar inlecelenip sonuçlar tablolar ile desteklenerek yorumlandı.
Makine öğrenmesi sınıflandırma yöntemlerinden Naive Bayes, Bayes NET, kNN,
Random Forest ve LSVM kullanıldı.
References
- [1]Bhattacharjee, A., Richards, W. G., Staunton, J., Li, C., Monti, S., Vasa, P., ... & Loda, M. (2001). Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proceedings of the National Academy of Sciences, 98(24), 13790-13795.[2] Huang, C. L., & Wang, C. J. (2006). A GA-based feature selection and parameters optimizationfor support vector machines. Expert Systems with applications, 31(2), 231-240.[3] Zhang, M. L., & Zhou, Z. H. (2007). ML-KNN: A lazy learning approach to multi-label learning. Pattern recognition, 40(7), 2038-2048.[4] Oshiro, T. M., Perez, P. S., & Baranauskas, J. A. (2012, July). How many trees in a random forest?. In International Workshop on Machine Learning and Data Mining in Pattern Recognition(pp. 154-168). Springer, Berlin, Heidelberg.[5] Valdes, A. D. J., Fong, M. W., & Porras, P. A. (2008). U.S. Patent No. 7,379,993. Washington, DC: U.S. Patent and Trademark Office.[6] Leung, K. M. (2007). Naive bayesian classifier. Polytechnic University Department of Computer Science/Finance and Risk Engineering.
Year 2019,
Volume: 2 Issue: 1
,
30
-
34
,
13.07.2019
Abstract
References
- [1]Bhattacharjee, A., Richards, W. G., Staunton, J., Li, C., Monti, S., Vasa, P., ... & Loda, M. (2001). Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proceedings of the National Academy of Sciences, 98(24), 13790-13795.[2] Huang, C. L., & Wang, C. J. (2006). A GA-based feature selection and parameters optimizationfor support vector machines. Expert Systems with applications, 31(2), 231-240.[3] Zhang, M. L., & Zhou, Z. H. (2007). ML-KNN: A lazy learning approach to multi-label learning. Pattern recognition, 40(7), 2038-2048.[4] Oshiro, T. M., Perez, P. S., & Baranauskas, J. A. (2012, July). How many trees in a random forest?. In International Workshop on Machine Learning and Data Mining in Pattern Recognition(pp. 154-168). Springer, Berlin, Heidelberg.[5] Valdes, A. D. J., Fong, M. W., & Porras, P. A. (2008). U.S. Patent No. 7,379,993. Washington, DC: U.S. Patent and Trademark Office.[6] Leung, K. M. (2007). Naive bayesian classifier. Polytechnic University Department of Computer Science/Finance and Risk Engineering.
There are 1 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Research Article |
| Authors | |
| Publication Date | July 13, 2019 |
| IZ | https://izlik.org/JA83JM75FH |
| Published in Issue | Year 2019 Volume: 2 Issue: 1 |