Abstract
Bu çalışmada Genetik Algoritma
ve Destek Vektör Makinelerinden oluşan melez bir yöntemin CUDA tabanlı hız optimizasyonu
gerçekleştirilmiştir. Makine öğrenmesinde, geliştirilen yöntemlerin yüksek doğruluk
değerlerinde başarı vermesi hedeflenir. Ayrıca önerilen algoritmanın sonuçları bulurken
hızlı bir şekilde çalışması da yine hedeflenen bir durumdur. Bu çalışmada, özellikle
gerçek zamanlı uygulamalarda önemli bir parametre olan hız parametresi dikkate alınmakta
ve verilerin hızlı bir şekilde sınıflandırılması için yeni bir GPU teknolojisi kullanılmaktadır.
Bunun için grafik işlemciler üzerinde programlama yapmamızı sağlayan CUDA programlamadan
yararlanılmıştır. Sınıflandırma algoritması olarak genetik algoritmayla optimize
edilmiş destek vektör makinesi kullanılmıştır. Deneyler 384 CUDA çekirdeğinden oluşan
NVIDIA GeForce 940MX ekran kartına sahip bir bilgisayar üzerinde gerçekleştirilmiştir.
Büyük ölçekli veri kümeleri üzerinde yapılan deneylerde, CUDA programlamanın sonuçlar
üzerinde pozitif etkilerinin olduğu görülmüştür. Bu şekilde makine öğrenmesi uygulamalarında
sınıflandırma aşamasında grafik işlemciler ile gerçek zamanlı uygulamalar için hızlı
bir sistemin altyapısı oluşturulabilir.
Keywords
References
- Lo, W. T., Chang, Y. S., Sheu, R. K., Chiu, C. C., & Yuan, S. M. (2014). CUDT: a CUDA based decision tree algorithm. The Scientific World Journal, 2014.
- Sierra-Canto, Xavier, Madera-Ramirez, Francisco, V. Uc-Cetina, Parallel training of a back-propagation neural network using cuda, in: Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications, ICMLA ’10, IEEE Computer Society, Washington, DC, USA, 2010, pp. 307–312.
- J. Bhimani, M. Leeser and N. Mi, "Accelerating K-Means clustering with parallel implementations and GPU computing," in High Performance Extreme Computing Conference (HPEC), 2015 IEEE, 2015, pp. 1-6.
- J. Zhang, G. Wu, X. Hu, S. Li and S. Hao, "A parallel K-Means clustering algorithm with MPI," in Parallel Architectures, Algorithms and Programming (PAAP), 2011 Fourth International Symposium on, 2011, pp. 60-64.
- B. Catanzaro, N. Sundaram, and K. Keutzer, \Fast support vector machine training and classification on graphics processors," in Proceedings of the 25th international conference on Machine learning, ICML ’08, (New York, NY, USA), pp. 104{111, ACM, 2008.
- L. J. Cao, S. S. Keerthi, C.-J. Ong, J. Q. Zhang, U. Periyathamby, X. J. Fu, and H. P. Lee, \Parallel sequential minimal optimization for the training of support vector machines," Neural Networks, IEEE Transactions on, vol. 17, pp. 1039{1049, July 2006.
- T. He, Z. Dong, K. Meng, H. Wang, Y. Oh, Accelerating multi-layer perceptron based short term demand forecasting using graphics processing units, in: Transmission & Distribution Conference & Exposition: Asia and Pacific, 2009, IEEE, 2009, pp. 1–4.
- Ruiz-Gonzalez, R.; Gomez-Gil, J.; Gomez-Gil, F.J.; Martínez-Martínez, V. An SVM-Based Classifier for Estimating the State of Various Rotating Components in Agro-Industrial Machinery with a Vibration Signal Acquired from a Single Point on the Machine Chassis. Sensors 2014, 14, 20713-20735.
- Liu M., Wu C., (2003), “Scheduling algorithm based on evolutionary computing in identical parallel machine production line”, Robotics and Computer Integrated Manufacturing, 19, 6-7.
- Lessmann, S., Stahlbock, R., and Crone, S. F.: Optimizing hyperparameters of support vector machines by genetic algorithms, In IC-AI, 74–82, 2005
- Pourbasheer, E., Riahi, S., Ganjali, M. R., and Norouzi, P.: Application of genetic algorithm support vector machine (GA-SVM) for prediction of BK-channels activity, Euro. J. Medicinal Chem., 44, 5023–5028, 2009.
- NVIDIA CUDA, https://docs.nvidia.com/cuda/, Erişim Tarihi: 10.12.2017
Abstract
In this study, CUDA based
speed optimization of a hybrid method consisting of Genetic Algorithm and Support
Vector Machines has been performed. In machine learning, it is aimed to achieve
high accuracy values from the developed methods. It is also a target for the proposed
algorithm to work quickly while finding the results. In this study, speed parameter
which is indispensable especially in real time applications is taken into consideration
and a new GPU technology is used to classify the data quickly. Therefore, CUDA programming,
which allows us to program on graphics processors of which importance and use are
increasing in recent years, has been benefited from. Support vector machine optimized
by genetic algorithm has been used as the classification algorithm. The experiments
have been performed on a computer with NVIDIA GeForce 940MX graphics card, which
consists of 384 CUDA core. Experiments performed on large scale data sets have shown
that CUDA programming has positive effects on the results. In this way, the infrastructure
of a quick system for real-time applications can be created by using the graphics
processors in the classification phase of the machine learning applications.
Keywords
References
- Lo, W. T., Chang, Y. S., Sheu, R. K., Chiu, C. C., & Yuan, S. M. (2014). CUDT: a CUDA based decision tree algorithm. The Scientific World Journal, 2014.
- Sierra-Canto, Xavier, Madera-Ramirez, Francisco, V. Uc-Cetina, Parallel training of a back-propagation neural network using cuda, in: Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications, ICMLA ’10, IEEE Computer Society, Washington, DC, USA, 2010, pp. 307–312.
- J. Bhimani, M. Leeser and N. Mi, "Accelerating K-Means clustering with parallel implementations and GPU computing," in High Performance Extreme Computing Conference (HPEC), 2015 IEEE, 2015, pp. 1-6.
- J. Zhang, G. Wu, X. Hu, S. Li and S. Hao, "A parallel K-Means clustering algorithm with MPI," in Parallel Architectures, Algorithms and Programming (PAAP), 2011 Fourth International Symposium on, 2011, pp. 60-64.
- B. Catanzaro, N. Sundaram, and K. Keutzer, \Fast support vector machine training and classification on graphics processors," in Proceedings of the 25th international conference on Machine learning, ICML ’08, (New York, NY, USA), pp. 104{111, ACM, 2008.
- L. J. Cao, S. S. Keerthi, C.-J. Ong, J. Q. Zhang, U. Periyathamby, X. J. Fu, and H. P. Lee, \Parallel sequential minimal optimization for the training of support vector machines," Neural Networks, IEEE Transactions on, vol. 17, pp. 1039{1049, July 2006.
- T. He, Z. Dong, K. Meng, H. Wang, Y. Oh, Accelerating multi-layer perceptron based short term demand forecasting using graphics processing units, in: Transmission & Distribution Conference & Exposition: Asia and Pacific, 2009, IEEE, 2009, pp. 1–4.
- Ruiz-Gonzalez, R.; Gomez-Gil, J.; Gomez-Gil, F.J.; Martínez-Martínez, V. An SVM-Based Classifier for Estimating the State of Various Rotating Components in Agro-Industrial Machinery with a Vibration Signal Acquired from a Single Point on the Machine Chassis. Sensors 2014, 14, 20713-20735.
- Liu M., Wu C., (2003), “Scheduling algorithm based on evolutionary computing in identical parallel machine production line”, Robotics and Computer Integrated Manufacturing, 19, 6-7.
- Lessmann, S., Stahlbock, R., and Crone, S. F.: Optimizing hyperparameters of support vector machines by genetic algorithms, In IC-AI, 74–82, 2005
- Pourbasheer, E., Riahi, S., Ganjali, M. R., and Norouzi, P.: Application of genetic algorithm support vector machine (GA-SVM) for prediction of BK-channels activity, Euro. J. Medicinal Chem., 44, 5023–5028, 2009.
- NVIDIA CUDA, https://docs.nvidia.com/cuda/, Erişim Tarihi: 10.12.2017
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Engineering |
| Journal Section | Tasarım ve Teknoloji |
| Authors | |
| Publication Date | September 30, 2018 |
| Submission Date | February 1, 2018 |
| Published in Issue | Year 2018 |
