Abstract
Twin bounded large distribution machine (TBLDM) considers structural risk and marginal distribution issues, obtaining good efficiency, robustness, and generalization performance. However, it ignores the influence of noise and uncertainty of the input data, which is inevitable in reality. Aiming at the problem, this paper introduces the idea of fuzzy set theory and proposes a novel fuzzy TBLDM (FTBLDM). The fuzzy membership function is set to be related to the distance of the sample to the class center, which describes its importance and credibility level. By incorporating the membership degree into the object function, FTBLDM reduces the influence of outliers and noise on the optimal hyperplane. The effectiveness of our method is validated by experiments on a synthetic dataset and UCI benchmark datasets. Besides, to verify the anti-noise performance of the model, we conduct experiments on UCI datasets with noise. The results show that FTBLDM is able to produce promising results compared to several benchmark and state-of-the-art algorithms.
This work is supported by the National Nature Science Foundation of China (No. 62106205), Fundamental Research Funds for the Central Universities (No. SWU021002), Natural Science Foundation of Chongqing (Nos. cstc2021jcyj-msxmX0824 and cstc2021jcyj-msxmX0565), the project of science and technology research program of Chongqing Education Commission of China (Nos. KJQN202100207 and KJZD-K202100203), and the Chongqing Municipal Training Program of Innovation and Entrepreneurship for Undergraduate (Nos. S202210635266, and X202210635352).
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Jin, Q., Fan, S., Dong, D., Zhang, L. (2022). Fuzzy Twin Bounded Large Margin Distribution Machines. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13534. Springer, Cham. https://doi.org/10.1007/978-3-031-18907-4_17
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