Optimization of Fuzzy Support Vector Machine (FSVM) Performance by Distance-Based Similarity Measure Classification

Sugiyarto Surono, Tia Nursofiyani, Annisa E. Haryati


This research aims to determine the maximum or minimum value of a Fuzzy Support Vector Machine (FSVM) Algorithm using the optimization function. SVM is considered as an effective method of data classification, as opposed to FSVM, which is less effective on large and complex data because of its sensitivity to outliers and noise. One of the techniques used to overcome this inefficiency is fuzzy logic with its ability to select the right membership function, which significantly affects the effectiveness of the FSVM algorithm performance. This research was carried out using the Gaussian membership function and the Distance-Based Similarity Measurement consisting of the Euclidean, Manhattan, Chebyshev, and Minkowsky distance methods. Subsequently, the optimization of the FSVM classification process was determined using four proposed FSVM models and normal SVM as comparison references. The results showed that the method tends to eliminate the impact of noise and enhance classification accuracy effectively. FSVM provides the best and highest accuracy value of 94% at a penalty parameter value of 1000 using the Chebyshev distance matrix. Furthermore, the model proposed will be compared to the performance evaluation model in preliminary studies (Xiao Kang et al., 2018). The result further showed that using FSVM with Chebyshev distance matrix and a Gaussian membership function provides a better performance evaluation value.


Doi: 10.28991/HIJ-2021-02-04-02

Full Text: PDF


FSVM; Membership Function Fuzzy; Classification; Distance-based Similarity Measure.


Silva, I., & Naranjo, J. E. (2020). A systematic methodology to evaluate prediction models for driving style classification. Sensors (Switzerland), 20(6), 1–21. doi:10.3390/s20061692.

Asri, H., Mousannif, H., Al Moatassime, H., & Noel, T. (2016). Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis. Procedia Computer Science, 83(Fams), 1064–1069. doi:10.1016/j.procs.2016.04.224.

Fauzi, F. (2017). K-Nearset Neighbor (K-NN) dan Support Vector Machine (SVM) untuk Klasifikasi Indeks Pembangunan Manusia Provinsi Jawa Tengah. Jurnal Mipa, 40(2), 118–124.

Han, K. X., Chien, W., Chiu, C. C., & Cheng, Y. T. (2020). Application of support vector machine (SVM) in the sentiment analysis of twitter dataset. Applied Sciences (Switzerland), 10(3). doi:10.3390/app10031125.

Lin, Y., Yu, H., Wan, F., & Xu, T. (2017). Research on Classification of Chinese Text Data Based on SVM. IOP Conference Series: Materials Science and Engineering, 231(1), 0–5. doi:10.1088/1757-899X/231/1/012067.

Widiastuti, N. I., Rainarli, E., & Dewi, K. E. (2017). Peringkasan dan Support Vector Machine pada Klasifikasi Dokumen. Jurnal Infotel, 9(4), 416. doi:10.20895/infotel.v9i4.312.

Pavithra, S., & Janakiraman, S. (2020). Enhanced polynomial kernel (EPK)–based support vector machine (SVM) (EPK-SVM) classification technique for speech recognition in hearing-impaired listeners. Concurrency and Computation: Practice and Experience, 1–12,. doi:10.1002/cpe.5210.

Orlando, G., Raimondi, D., Khan, T., Lenaerts, T., & Vranken, W. F. (2017). SVM-dependent pairwise HMM: An application to protein pairwise alignments. Bioinformatics, 33(24), 3902–3908. doi:10.1093/bioinformatics/btx391.

Sohail, A., & Arif, F. (2020). Supervised and unsupervised algorithms for bioinformatics and data science. Progress in Biophysics and Molecular Biology, 151, 14–22. doi:10.1016/j.pbiomolbio.2019.11.012.

Lin, X., Li, C., Zhang, Y., Su, B., Fan, M., & Wei, H. (2018). Selecting feature subsets based on SVM-RFE and the overlapping ratio with applications in bioinformatics. Molecules, 23(1). doi:10.3390/molecules23010052.

Akbani, R., & Korkmaz, T. (2010). Applications of Support Vector Machines in Bioinformatics and Network Security. Application of Machine Learning, February. doi:10.5772/8618.

Vanpik, V., & Cortes, C. (1995). Support Vector Network. Mach. Learn, 20, 273–297.

Pushpita Anna Octaviani, Yuciana Wilandari, D. I. (2014). Penerapan Metode SVM Pada Data Akreditasi Sekolah Dasar Di Kabupaten Magelang. Jurnal Gaussian, 3(8), 811–820.

Brereton, R. G., & Lloyd, G. R. (2010). Support Vector Machines for classification and regression. Analyst, 135(2), 230–267. doi:10.1039/b918972f.

Wu, Q., & Wang, W. (2013). Piecewise-smooth support vector machine for classification. Mathematical Problems in Engineering, 2013. doi:10.1155/2013/135149.

Lin, C. F., & Wang, S. De. (2002). Fuzzy support vector machines. IEEE Transactions on Neural Networks, 13(2), 464–471. doi:10.1109/72.991432.

An, W., & Liang, M. (2013). Fuzzy support vector machine based on within-class scatter for classification problems with outliers or noises. Neurocomputing, 110, 101–110. doi:10.1016/j.neucom.2012.11.023.

Guernine, T., & Zeroual, K. (2011). New fuzzy multi-class method to train SVM classifier. 3rd International Conference on Advances in Databases, Knowledge, and Data Applications, 77–82.

Castro, J. L., Flores-Hidalgo, L. D., Mantas, C. J., & Puche, J. M. (2007). Extraction of fuzzy rules from support vector machines. Fuzzy Sets and Systems, 158(18), 2057–2077. doi:10.1016/j.fss.2007.04.014.

Wu, K., Zhou, M., Sean Lu, X., & Huang, L. (2017). Fuzzy logic-based text classification method for social media data. 2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017, 2017-January, 1942–1947. doi:10.1109/SMC.2017.8122902.

Sridevi, P. (2019). Identification of suitable membership and kernel function for FCM based FSVM classifier model. Cluster Computing, 22, 11965–11974. doi:10.1007/s10586-017-1533-9.

Gupta, D., Richhariya, B., & Borah, P. (2019). A fuzzy twin support vector machine based on information entropy for class imbalance learning. Neural Computing and Applications, 31(11), 7153–7164. doi:10.1007/s00521-018-3551-9.

Ding, X. K., Yang, X. J., Jiang, J. Y., Deng, X. L., Cai, J. C., & Ji, Y. Y. (2018). Optimization and analysis on fuzzy SVM for objects classification. Journal of Information Hiding and Multimedia Signal Processing, 9(6), 1421–1429.

Wang, R., Zhang, X., & Cao, W. (2016). Clifford Fuzzy Support Vector Machines for Classification. Advances in Applied Clifford Algebras, 26(2), 825–846. doi:10.1007/s00006-015-0616-z.

Xiaokang, D., Lei, Y., Jianping, Y., & Zhaozhong, Z. (2016). Optimization and Analysis on Fuzzy SVM for Targets Classification in Forest. The Open Cybernetics & Systemics Journal, 10(1), 155–162. doi:10.2174/1874110x01610010155.

Jiang, X., Yi, Z., & Lv, J. C. (2006). Fuzzy SVM with a new fuzzy membership function. Neural Computing and Applications, 15(3–4), 268–276. doi:10.1007/s00521-006-0028-z.

Al-Mumtazah, N. S., & Surono, S. (2020). Quadratic Form Optimization with Fuzzy Number Parameters: Multiobjective Approaches. International Journal of Fuzzy Systems, 22(4), 1191–1197. doi:10.1007/s40815-020-00808-x.

Liu, W., Ci, L. L., & Liu, L. P. (2020). A new method of fuzzy support vector machine algorithm for intrusion detection. Applied Sciences (Switzerland), 10(3). doi:10.3390/app10031065.

Mohammed, N. N., & Abdulazeez, A. M. (2018). Evaluation of partitioning around medoids algorithm with various distances on microarray data. Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, IThings-GreenCom-CPSCom-SmartData 2017, 2018-January, 1011–1016. doi:10.1109/iThings-GreenCom-CPSCom-SmartData.2017.155.

Syaripudin, U., Badruzaman, I., Yani, E., K, D., & Ramdhani, M. (2013). Studi Komparatif Penerapan Metode Hierarchical, K-Means Dan Self Organizing Maps (SOM) Clustering Pada Basis Data. Istek, VII(1), 132–149.

Full Text: PDF

DOI: 10.28991/HIJ-2021-02-04-02


  • There are currently no refbacks.

Copyright (c) 2021 Sugiyarto Surono, Tia Nursofiyani, Annisa Eka Haryati