直觉模糊的结构化最小二乘孪生支持向量机

doi:10.3969/j.issn.0255-8297.2024.02.015

Abstract

Abstract: Addressing the sensitivity of the least squares twin support vector machine(LS-SVM) to noise or abnormal data, and its tendency to overlook intrinsic structural information in the data, this paper introduces an intuition fuzzy and structural least squares twin support vector machine(IF-SLSTSVM). Firstly, the input sample points undergo preprocessing using isolated forest. Subsequently, leveraging the concept of intuitionistic fuzzy, varying weights are assigned to the input sample points to mitigate the impact of noise or abnormal data on the classification hyperplane. Finally, the K-Means algorithm is employed to extract structural information, represented in the form of covariance, among the input sample points. Built upon LS-SVM, IF-SLSTSVM takes into account the distribution information of input sample points in the feature space and their interrelationships,thereby enhancing the model's robustness. Experimental validation is performed using the UCI dataset in noise environments with different proportions of 0%, 5%, 10%, and 20%. The results demonstrate that the IF-SLSTSVM algorithm exhibits superior robustness compared to six other evaluated algorithms.

Key words: support vector machine, isolated forest, structural information, intuition fuzzy, clustering, covariance

CLC Number:

TP391

ZHANG Faying, LYU Li, HAN Longzhe, LIU Dongxiao, FAN Tanghuai. Intuition Fuzzy and Structural Least Squares Twin Support Vector Machine[J]. Journal of Applied Sciences, 2024, 42(2): 350-363.

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Intuition Fuzzy and Structural Least Squares Twin Support Vector Machine

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