Abstract
Random vector functional link (RVFL) and its kernelized version, i.e., kernel-based RVFL (K-RVFL) are popular machine learning models for classification as well as regression. This work presents a new K-RVFL-based classification model with intuitionistic fuzzy membership called the intuitionistic fuzzy K-RVFL (IFK-RVFL) classifier. In IFK-RVFL, an intuitionistic fuzzy number is linked with each training data point which can be either membership or non-membership. IFK-RVFL uses the least-squares approach for finding a solution rather than solving quadratic programming problems (QPPs) unlike support vector machine (SVM) and twin SVM (TWSVM). In IFK-RVFL, the kernel function is introduced to handle the nonlinearity. The proposed IFK-RVFL is novel and has a low computational cost. Experimental simulations have been carried out on a few popular benchmark datasets. The obtained classification accuracies of IFK-RVFL are statistically compared with intuitionistic fuzzy SVM (IFSVM), intuitionistic fuzzy twin SVM (IFTSVM), RVFL, and K-RVFL models. The experimental outcomes portray the efficacy and applicability of the proposed IFK-RVFL model.
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References
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297
Gupta U, Gupta D (2019) Lagrangian twin-bounded support vector machine based on L2-norm. In: Recent developments in machine learning and data analytics. Springer, Singapore, pp 431–444
Hazarika BB, Gupta D (2021) Density-weighted support vector machines for binary class imbalance learning. Neural Comput Appl 33(9):4243–4261
Borah P, Gupta D (2019) Functional iterative approaches for solving support vector classification problems based on generalized Huber loss. Neural Comput Appl 1–21
Hazarika BB, Gupta D, Berlin M (2020) A comparative analysis of artificial neural network and support vector regression for river suspended sediment load prediction. In: First international conference on sustainable technologies for computational intelligence. Springer, Singapore, pp 339–349
Gupta U, Gupta D (2021) Kernel-target alignment based fuzzy Lagrangian twin bounded support vector machine. Int J Uncertainty Fuzziness Knowl Based Syst 29(05):677–707
Gupta U, Gupta D, Prasad M (2018) Kernel target alignment based fuzzy least square twin bounded support vector machine. In: 2018 IEEE Symposium series on computational intelligence (SSCI). IEEE, pp 228–235
Lin C-F, Wang S-D (2002) Fuzzy support vector machines. IEEE Trans Neural Networks 13(2):464–471
Atanassov KT (1999) Intuitionistic fuzzy sets. In: Intuitionistic fuzzy sets. Physica, Heidelberg, pp 1–137
Ha M, Wang C, Chen J (2013) The support vector machine based on intuitionistic fuzzy number and kernel function. Soft Comput 17(4):635–641
Ha MH, Huang S, Wang C, Wang XL (2011) Intuitionistic fuzzy support vector machine. J Hebei Univ (Nat Sci Ed) 3:225–229
Rezvani S, Wang X, Pourpanah F (2019) Intuitionistic fuzzy twin support vector machines. IEEE Trans Fuzzy Syst 27(11):2140–2151
Pao Y-H, Phillips SM, Sobajic DJ (1992) Neural-net computing and the intelligent control of systems. Int J Control 56(2):263–289
Xu KK, Li HX, Yang HD (2017) Kernel-based random vector functional-link network for fast learning of spatiotemporal dynamic processes. IEEE Trans Syst Man Cybern Syst 49(5):1016–1026
Chauhan V, Tiwari A, Arya S (2020) Multi-label classifier based on kernel random vector functional link network. In: 2020 International joint conference on neural networks (IJCNN). IEEE, pp 1–7
Hazarika BB, Gupta D, Borah P (2021) An intuitionistic fuzzy kernel ridge regression classifier for binary classification. Appl Soft Comput 112:107816
Dua D, Graff C (2017) UCI machine learning repository
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Hazarika, B.B., Gupta, D., Gupta, U. (2023). Intuitionistic Fuzzy Kernel Random Vector Functional Link Classifier. In: Sisodia, D.S., Garg, L., Pachori, R.B., Tanveer, M. (eds) Machine Intelligence Techniques for Data Analysis and Signal Processing. Lecture Notes in Electrical Engineering, vol 997. Springer, Singapore. https://doi.org/10.1007/978-981-99-0085-5_72
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DOI: https://doi.org/10.1007/978-981-99-0085-5_72
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