How to cite this paper
Sain, H., Kuswanto, H., Purnami, S & Rahaya, S. (2023). Fuzzy support vector machine for classification of time series data: A simulation study.Decision Science Letters , 12(3), 487-498.
Refrences
Abe, S., & Inoue, T. (2002, April). Fuzzy support vector machines for multiclass problems. In ESANN (pp. 113-118).
Batuwita, R., & Palade, V. (2010). FSVM-CIL: fuzzy support vector machines for class imbalance learning. IEEE Transactions on Fuzzy Systems, 18(3), 558-571.
Bostrom, A., & Bagnall, A. (2017). Binary shapelet transform for multiclass time series classification. Transactions on Large-Scale Data-and Knowledge-Centered Systems XXXII: Special Issue on Big Data Analytics and Knowledge Discovery, 24-46.
Crammer, K., & Singer, Y. (2001). On the algorithmic implementation of multiclass kernel-based vector machines. Journal of machine learning research, 2(Dec), 265-292.
Fan, Q., Wang, Z., Li, D., Gao, D., & Zha, H. (2017). Entropy-based fuzzy support vector machine for imbalanced datasets. Knowledge-Based Systems, 115, 87-99.
Farquad, M. A. H., & Bose, I. (2012). Preprocessing Imbalanced data using support vector machine. Decision Support Systems, 53(1), 226-233.
Hills, J., Lines, J., Baranauskas, E., Mapp, J., & Bagnall, A. (2014). Classification of time series by shapelet transformation. Data mining and knowledge discovery, 28, 851-881.
Horng, M. H. (2009). Multi-class support vector machine for classification of the ultrasonic images of supraspinatus. Expert Systems with Applications, 36(4), 8124-8133.
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P. A. (2019). Deep learning for time series classification: a review. Data mining and knowledge discovery, 33(4), 917-963.
Jeong, Y. S., Jeong, M. K., & Omitaomu, O. A. (2011). Weighted dynamic time warping for time series classification. Pattern recognition, 44(9), 2231-2240.
Kreßel, U. G. (1999). Pairwise classification and support vector machines. Advances in kernel methods: support vector learning, 255-268.
Lee, T. S., Chiu, C. C., Chou, Y. C., & Lu, C. J. (2006). Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Computational Statistics & Data Analysis, 50(4), 1113-1130.
Lin, C. F., & Wang, S. D. (2002). Fuzzy support vector machines. IEEE transactions on neural networks, 13(2), 464-471.
Lines, J., Davis, L. M., Hills, J., & Bagnall, A. (2012, August). A shapelet transform for time series classification. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 289-297).
Mandal, S. N., Choudhury, J. P., & Chaudhuri, S. B. (2012). In search of suitable fuzzy membership function in prediction of time series data. International Journal of Computer Science Issues, 9(3), 293-302.
Mohammadi, M., & Sarmad, M. (2019). Robustified distance based fuzzy membership function for support vector machine classification. Iranian Journal of Fuzzy Systems, 16(6), 191-204.
Sain, H., & Purnami, S. W. (2015). Combine sampling support vector machine for imbalanced data classification. Procedia Computer Science, 72, 59-66.
Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information processing & management, 45(4), 427-437.
Tsang, E. C., Yeung, D. S., & Chan, P. P. (2003, November). Fuzzy support vector machines for solving two-class problems. In Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No. 03EX693) (Vol. 2, pp. 1080-1083). IEEE.
Vapnik, V. N. (1995). The nature of statistical learning theory. 840 Springer-Verlag New York. Inc., New York, NY, USA, 841, 842.
Weston, J., & Watkins, C. (1999, April). Support vector machines for multi-class pattern recognition. In Esann (Vol. 99, pp. 219-224).
Wu, Y., Shen, L., & Zhang, S. (2017, May). Fuzzy multiclass support vector machines for unbalanced data. In 2017 29th Chinese Control And Decision Conference (CCDC) (pp. 2227-2231). IEEE.
Xue, X., Yang, X., & Chen, X. (2014). Application of a support vector machine for prediction of slope stability. Science China Technological Sciences, 57, 2379-2386.
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
Zhang, D., Zuo, W., Zhang, D., & Zhang, H. (2010, August). Time series classification using support vector machine with Gaussian elastic metric kernel. In 2010 20th International Conference on Pattern Recognition (pp. 29-32). IEEE.
Batuwita, R., & Palade, V. (2010). FSVM-CIL: fuzzy support vector machines for class imbalance learning. IEEE Transactions on Fuzzy Systems, 18(3), 558-571.
Bostrom, A., & Bagnall, A. (2017). Binary shapelet transform for multiclass time series classification. Transactions on Large-Scale Data-and Knowledge-Centered Systems XXXII: Special Issue on Big Data Analytics and Knowledge Discovery, 24-46.
Crammer, K., & Singer, Y. (2001). On the algorithmic implementation of multiclass kernel-based vector machines. Journal of machine learning research, 2(Dec), 265-292.
Fan, Q., Wang, Z., Li, D., Gao, D., & Zha, H. (2017). Entropy-based fuzzy support vector machine for imbalanced datasets. Knowledge-Based Systems, 115, 87-99.
Farquad, M. A. H., & Bose, I. (2012). Preprocessing Imbalanced data using support vector machine. Decision Support Systems, 53(1), 226-233.
Hills, J., Lines, J., Baranauskas, E., Mapp, J., & Bagnall, A. (2014). Classification of time series by shapelet transformation. Data mining and knowledge discovery, 28, 851-881.
Horng, M. H. (2009). Multi-class support vector machine for classification of the ultrasonic images of supraspinatus. Expert Systems with Applications, 36(4), 8124-8133.
Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P. A. (2019). Deep learning for time series classification: a review. Data mining and knowledge discovery, 33(4), 917-963.
Jeong, Y. S., Jeong, M. K., & Omitaomu, O. A. (2011). Weighted dynamic time warping for time series classification. Pattern recognition, 44(9), 2231-2240.
Kreßel, U. G. (1999). Pairwise classification and support vector machines. Advances in kernel methods: support vector learning, 255-268.
Lee, T. S., Chiu, C. C., Chou, Y. C., & Lu, C. J. (2006). Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Computational Statistics & Data Analysis, 50(4), 1113-1130.
Lin, C. F., & Wang, S. D. (2002). Fuzzy support vector machines. IEEE transactions on neural networks, 13(2), 464-471.
Lines, J., Davis, L. M., Hills, J., & Bagnall, A. (2012, August). A shapelet transform for time series classification. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 289-297).
Mandal, S. N., Choudhury, J. P., & Chaudhuri, S. B. (2012). In search of suitable fuzzy membership function in prediction of time series data. International Journal of Computer Science Issues, 9(3), 293-302.
Mohammadi, M., & Sarmad, M. (2019). Robustified distance based fuzzy membership function for support vector machine classification. Iranian Journal of Fuzzy Systems, 16(6), 191-204.
Sain, H., & Purnami, S. W. (2015). Combine sampling support vector machine for imbalanced data classification. Procedia Computer Science, 72, 59-66.
Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information processing & management, 45(4), 427-437.
Tsang, E. C., Yeung, D. S., & Chan, P. P. (2003, November). Fuzzy support vector machines for solving two-class problems. In Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No. 03EX693) (Vol. 2, pp. 1080-1083). IEEE.
Vapnik, V. N. (1995). The nature of statistical learning theory. 840 Springer-Verlag New York. Inc., New York, NY, USA, 841, 842.
Weston, J., & Watkins, C. (1999, April). Support vector machines for multi-class pattern recognition. In Esann (Vol. 99, pp. 219-224).
Wu, Y., Shen, L., & Zhang, S. (2017, May). Fuzzy multiclass support vector machines for unbalanced data. In 2017 29th Chinese Control And Decision Conference (CCDC) (pp. 2227-2231). IEEE.
Xue, X., Yang, X., & Chen, X. (2014). Application of a support vector machine for prediction of slope stability. Science China Technological Sciences, 57, 2379-2386.
Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
Zhang, D., Zuo, W., Zhang, D., & Zhang, H. (2010, August). Time series classification using support vector machine with Gaussian elastic metric kernel. In 2010 20th International Conference on Pattern Recognition (pp. 29-32). IEEE.