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<p>To mitigate the negative impact of noise on twin support vector machines (TWSVM), researchers have integrated fuzzy set theory with TWSVM, utilizing fuzzy membership degrees to characterize the influence of various samples in constructing the optimal hyperplane. This approach leads to the development of a fuzzy twin support vector machine (FTSVM). However, existing fuzzy membership degree assignment strategies have several drawbacks: (1) The geometric distance-based assignment strategy exhibits high computational complexity and neglects the surrounding environment of data points. (2) The information entropy-based assignment strategy is sensitive to sample variations and poses challenges in selecting the appropriate membership functions. To address these issues, this study introduces a mass-based dissimilarity measure into the fuzzy membership degree assignment process. The primary factor for assessing the dissimilarity between two instances is the minimum area probability block that encompasses both instances. Then this assignment strategy is integrated with the TWSVM and a novel fuzzy twin support vector machine using mass-based dissimilarity measure (MDFTSVM) is proposed. Additionally, MDFTSVM employs a coordinate descent strategy with shrinking by an active set to reduce computational complexity and significantly improve model training speed. Experimental evaluations are conducted using artificially constructed datasets as well as UCI datasets, which confirm the effectiveness of MDFTSVM in addressing binary classification problems with noise. The results also demonstrate its superior robustness and generalization performance compared to support vector machine, fuzzy support vector machine, TWSVM, FTSVM, twin bounded support vector machine, fuzzy membership assignment strategy based on dissimilarity measure, and fuzzy twin support vector machine based on affinity and class probability models.</p>

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A novel fuzzy twin support vector machine using mass-based dissimilarity measure

  • 期刊论文

  • 发表日期：

    2025年1月28日

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  • Xia Wang,
  • Gaohao Wu,
  • Guosheng Hao,
  • Zichen Zhang

摘要

To mitigate the negative impact of noise on twin support vector machines (TWSVM), researchers have integrated fuzzy set theory with TWSVM, utilizing fuzzy membership degrees to characterize the influence of various samples in constructing the optimal hyperplane. This approach leads to the development of a fuzzy twin support vector machine (FTSVM). However, existing fuzzy membership degree assignment strategies have several drawbacks: (1) The geometric distance-based assignment strategy exhibits high computational complexity and neglects the surrounding environment of data points. (2) The information entropy-based assignment strategy is sensitive to sample variations and poses challenges in selecting the appropriate membership functions. To address these issues, this study introduces a mass-based dissimilarity measure into the fuzzy membership degree assignment process. The primary factor for assessing the dissimilarity between two instances is the minimum area probability block that encompasses both instances. Then this assignment strategy is integrated with the TWSVM and a novel fuzzy twin support vector machine using mass-based dissimilarity measure (MDFTSVM) is proposed. Additionally, MDFTSVM employs a coordinate descent strategy with shrinking by an active set to reduce computational complexity and significantly improve model training speed. Experimental evaluations are conducted using artificially constructed datasets as well as UCI datasets, which confirm the effectiveness of MDFTSVM in addressing binary classification problems with noise. The results also demonstrate its superior robustness and generalization performance compared to support vector machine, fuzzy support vector machine, TWSVM, FTSVM, twin bounded support vector machine, fuzzy membership assignment strategy based on dissimilarity measure, and fuzzy twin support vector machine based on affinity and class probability models.