GSA to Obtain SVM Kernel Parameter for Thyroid Nodule Classification

https://doi.org/10.22146/ijccs.41215

Dias Aziz Pramudita(1*), Aina Musdholifah(2)

(1) Program Studi S2 Ilmu Komputer FMIPA UGM, Yogyakarta
(2) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta, Indonesia
(*) Corresponding Author

Abstract


Support Vector Machine (SVM) is one of the most popular methods of classification problems due to its global optima solution. However, the selection of appropriate parameters and kernel values remains an obstacle in the process. The problem can be solved by adding the best value of parameter during optimization process in SVM. Gravitational Search Algorithm (GSA) will be used to optimize parameters of SVM. GSA is an optimization algorithm that is inspired by mass interaction and Newton's law of gravity. This research hybridizes the GSA and SVM  to increase system accuracy. The proposed approach had been implemented to improve the classification performance of Thyroid Nodule. The data used in this research are ultrasonography image of Thyroid Nodule obtained from RSUP Dr. Sardjito, Yogyakarta. This research had been evaluated by comparing the default SVM parameters with the proposed method in term of accuracy. The experiment results showed that the use of GSA on SVM is capable to increase system accuracy. In the polynomial kernel the accuracy rose up from 58.5366 % to 89.4309 %, and 41.4634 % to 98.374 % in Polynomial kernel

Keywords


Gravitational Search Algorithm; SVM; Thyroid Nodule; GSA-SVM

Full Text:

PDF


References

[1] Z. Tao, L. Huiling, W. Wenwen, and Y. Xia, “GA-SVM Based Feature Selection and Parameter Optimization in Hospitalization Expense Modeling,” Appl. Soft Comput. J., vol. 73, pp. 456–466, 2018. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1568494618306264. [Accessed: 20-Nov-2018].

[2] M. Aladeemy, S. Tutun, and M. T. Khasawneh, “A New Hybrid Approach for Feature Selection and Support Vector Machine Model Selection Based on Self-adaptive Cohort Intelligence,” vol. 88, pp. 118–131, 2017. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0957417417304475. [Accessed: 20-Sept-2017].

[3] S. Sarafrazi and H. Nezamabadi-Pour, “Facing the classification of binary problems with a GSA-SVM hybrid system,” Math. Comput. Model., vol. 57, no. 1–2, pp. 270–278, 2013. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S089571771100389X. [Accessed: 29-Aug-2017].

[4] B. R. Haugen, E. K. Alexander, K. C. Bible, G. M. Doherty, S. J. Mandel, Y. E. Nikiforov, F. Pacini, G. W. Randolph, A. M. Sawka, M. Schlumberger, K. G. Schuff, S. I. Sherman, J. A. Sosa, D. L. Steward, R. M. Tuttle, and L. Wartofsky, “2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer,” Thyroid, vol. 26, no. 1, pp. 1–133, 2016. [Online]. Available: https://www.ncbi.nlm.nih.gov/pubmed/26462967. [Accessed: 22-Sept-2017].

[5] Y. J. Choi, J. H. Baek, H. S. Park, W. H. Shim, T. Y. Kim, Y. K. Shong, and J. H. Lee, “A Computer-Aided Diagnosis System Using Artificial Intelligence for the Diagnosis and Characterization of Thyroid Nodules on Ultrasound: Initial Clinical Assessment,” Thyroid, vol. 27, no. 4, pp. 546–552, 2017. [Online]. Available: https://www.ncbi.nlm.nih.gov/pubmed/28071987. [Accessed: 22-Sept-2017].

[6] R. Gonzalez and R. Woods, Digital image processing, 4th Editio. Upper Saddle River, NJ, USA: Pearson, 2018.

[7] C. Cortes and V. Vapnik, “Support-Vector Networks,” Mach. Learn., vol. 20, no. 3, pp. 273–297, 1995. [Online]. Available: https://link.springer.com/article/10.1007/BF00994018. [Accessed: 20-Sept-2017].

[8] E. Prasetyo, Data Mining: Konsep dan Aplikasi menggunakan Matlab, 1st ed. Yogyakarta: ANDI, 2013.

[9] E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, “GSA: A Gravitational Search Algorithm,” Inf. Sci. (Ny)., vol. 179, no. 13, pp. 2232–2248, 2009. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0020025509001200. [Accessed: 20-Sept-2017].

[10] E. S. Wulandari, “Kesesuaian Penentuan Fitur Bentuk, Tepi dan Orientasi Nodul Tiroid Jinak dan Ganas Antara Pembacaan Ultrasonografi oleh Dokter Spesialis Radiologi dan Computer Aided Diagnostic (CAD),” Tesis, Fakultas Teknik, Universitas Gadjah Mada, 2017. [Online]. Available: http://etd.repository.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=128402&obyek_id=4. [Accessed: 21-Sept-2017].



DOI: https://doi.org/10.22146/ijccs.41215

Article Metrics

Abstract views : 2216 | views : 2300

Refbacks

  • There are currently no refbacks.




Copyright (c) 2020 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



Copyright of :
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 1978-1520 (print); ISSN 2460-7258 (online)
is a scientific journal the results of Computing
and Cybernetics Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijccs.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijccs



View My Stats1
View My Stats2