Robust fuzzy least square twin extreme learning machine

Abstract

To overcome the sensitivity of the least square twin extreme learning machine (LSTELM) to noise and outliers, a robust fuzzy least square twin extreme learning machine based on fuzzy hyperplane (RFHLSTELM) is proposed. The RFHLSTELM algorithm assigns appropriate weights to each sample according to the fuzzy memberships, which reduces the influence of noise effectively. Additionally, the hyperplane parameters are replaced by symmetric triangular fuzzy numbers, and the fuzzy decision boundary further improves the reliability of classification. Experimental results on synthetic, benchmark, and United States Postal Service (USPS) datasets with noise demonstrate that the RFHLSTELM algorithm outperforms existing algorithms on most datasets.

Keywords

extreme learning machine / symmetric triangular fuzzy number / fuzzy hyperplane / robustness

References

[1] SARKER I H . Machine learning: algorithms, real-world applications and research directions. SN Computer Science, 2021, 2(3): Article 160.

[2] LU J, MA G Z, ZHANG G Q . Fuzzy machine learning: a comprehensive framework and systematic review. IEEE Transactions on Fuzzy Systems, 2024, 32(7): 3861-3878.

[3] CERVANTES J, GARCIA-LAMONT F, RODRIGUEZ-MAZAHUA L, et al. A comprehensive survey on support vector machine classification: applications, challenges and trends. Neurocomputing, 2020, 408: 189-215.

[4] JAYADEVA, KHEMCHANDANI R, CHANDRA S. Twin support vector machines for pattern classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(5): 905-910.

[5] KUMAR M A, GOPAL M . Least squares twin support vector machines for pattern classification. Expert Systems with Applications, 2009, 36(4): 7535-7543.

[6] WAN Y H, SONG S J, HUANG G, et al. Twin extreme learning machines for pattern classification. Neurocomputing, 2017, 260: 235-244.

[7] HUANG G B, ZHOU H M, DING X J, et al. Extreme learning machine for regression and multiclass classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2011, 42(2): 513-529.

[8] RASTOGI R, BHARTI A . Least squares twin extreme learning machine for pattern classification. Innovations in Infrastructure: Proceedings of the 1st International Conference on Innovations in Infrastructure (ICIIF'18), 2018, May 18-19, Ahmedabad, India. AISC 757. Singapore: Springer, 2019: 561-571.

[9] RICHHARIYA B, TANVEER M . A robust fuzzy least squares twin support vector machine for class imbalance learning. Applied Soft Computing, 2018, 71: 418-432.

[10] ZHANG W B, JI H B . Fuzzy extreme learning machine for classification. Electronics Letters, 2013, 49(7): 448-450.

[11] OUYANG C S, KAO T C, CHENG Y Y, et al. An improved fuzzy extreme learning machine for classification and regression. Proceedings of the 2016 International Conference on Cybernetics, Robotics and Control (CRC'16), 2016, Aug 19-21, Hong Kong, China. Piscataway, NJ, USA: IEEE, 2016: 91-94.

[12] ATANASSOV K. Intuitionistic fuzzy sets. International Journal Bioautomation, 2016, 20(S1): 1-6.

[13] REZVANI S, Wang X Z, POURPANAH F . Intuitionistic fuzzy twin support vector machines. IEEE Transactions on Fuzzy Systems, 2019, 27(11): 2140-2151.

[14] GAO Q Y, AI Q, WANG W H . Intuitionistic fuzzy extreme learning machine with the truncated pinball loss. Neural Processing Letters, 2024, 56(2): Article 116.

[15] NALEPA J, KAWULOK M . Selecting training sets for support vector machines: a review. Artificial Intelligence Review, 2019, 52(2): 857-900.

[16] KUNG C F, HAO P Y . Fuzzy least squares support vector machine with fuzzy hyperplane. Neural Processing Letters, 2023, 55(6): 7415-7446.

[17] SARTAKHTI J S, AFRABANDPEY H, GHADIRI N . Fuzzy least squares twin support vector machines. Engineering Applications of Artificial Intelligence, 2019, 85: 402-409.

[18] HAO P Y, KUNG C F, CHANG C Y, et al. Predicting stock price trends based on financial news articles and using a novel twin support vector machine with fuzzy hyperplane. Applied Soft Computing, 2021, 98: Article 106806.

[19] DING S F, ZHAO H, ZHANG Y N, et al. Extreme learning machine: algorithm, theory and applications. Artificial Intelligence Review, 2015, 44: 103-115.

[20] KIM M. The generalized extreme learning machines: tuning hyperparameters and limiting approach for the Moore-Penrose generalized inverse. Neural Networks, 2021, 144: 591-602.

[21] ANAND P, BHARTI A, RASTOGI R . Time efficient variants of twin extreme learning machine. Intelligent Systems with Applications, 2023, 17: Article 200169.

[22] GUPTA U, GUPTA D . Regularized based implicit Lagrangian twin extreme learning machine in primal for pattern classification. International Journal of Machine Learning and Cybernetics, 2021, 12(5): 1311-1342.

[23] SINGH P K, SARKAR R, NASIPURI M . Statistical validation of multiple classifiers over multiple datasets in the field of pattern recognition. International Journal of Applied Pattern Recognition, 2015, 2(1): 1-23.

[24] ALA'RAJ M, ABBOD M F . Classifiers consensus system approach for credit scoring. Knowledge-Based Systems, 2016, 104: 89-105.