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Affinity-based elastic net intuitionistic fuzzy twin support vector machines

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Abstract

Intuitionistic fuzzy twin support vector machines (IFTWSVM) combined the concept of intuitionistic fuzzy sets with twin support vector machines (TWSVM) and showed excellent performance in classification. However, the existing intuitionistic fuzzy number schemes based on the single center and the local neighborhood of the sample are difficult to accurately reflect the location information of the sample, and the L1-norm penalty of the slack variable is not well defined from the point of view of geometric points. In view of the above deficiencies, we design a noval intuitionistic fuzzy number scheme and adopt elastic net to penalize the slack variables, propose Affinity-Based Elastic Net Intuitionistic Fuzzy Twin Support Vector Machines (AENIFTWSVM). It calculates the affinity of different classes of samples according to the Support Vector Data Description (SVDD) model in the kernel space, and considers the contribution of samples to the two classes, and the obtained affinity can be used to identify noise information. A series of experimental outcomes on benchmark datasets and handwritten digit dataset support that the proposed model outperforms some existing models.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 62172188).

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  1. Department of Mathematics, **an University, Guangzhou, 510632, China

    Zhishen Li & Peiai Zhang

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  1. Zhishen Li
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  2. Peiai Zhang
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Correspondence to Peiai Zhang.

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Li, Z., Zhang, P. Affinity-based elastic net intuitionistic fuzzy twin support vector machines. Int. J. Mach. Learn. & Cyber. 15, 2439–2455 (2024). https://doi.org/10.1007/s13042-023-02041-y

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