Fuzzy twin support vector machine (FTSVM) is an effective machine learning technique that is able to overcome the negative impact of noise and outliers in tackling data classification problems. In the FTSVM, the degree of membership function in the sample space describes the space between input data and class center, while ignoring the position of input data in the feature space and simply miscalculated the ledge support vectors as noises. This paper presents an intuitionistic FTSVM (IFTSVM) that combines the idea of intuitionistic fuzzy number with twin support vector machine (TSVM). An adequate fuzzy membership is employed to reduce the noise created by the pollutant inputs. Two functions, i.e., linear and nonlinear, are used to formulate two nonparallel hyperplanes. An IFTSVM not only reduces the influence of noises, it also distinguishes the noises from the support vectors. Further, this modification can minimize a newly formulated structural risk and improve the classification accuracy. Two artificial and eleven benchmark problems are employed to evaluate the effectiveness of the proposed IFTSVM model. To quantify the results statistically, the bootstrap technique with the 95% confidence intervals is used. The outcome shows that an IFTSVM is able to produce promising results as compared with those from the original support vector machine, fuzzy support vector machine, FTSVM, and other models reported in the literature.

中文翻译：

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直观模糊双支持向量机


模糊孪生支持向量机（FTSVM）是一种有效的机器学习技术，能够克服噪声和异常值在处理数据分类问题时的负面影响。在FTSVM中，样本空间中的隶属度函数描述了输入数据和类中心之间的空间，而忽略了输入数据在特征空间中的位置，并且简单地将壁架支持向量错误地计算为噪声。本文提出了一种将直觉模糊数与孪生支持向量机（TSVM）相结合的直觉FTSVM（IFTSVM）。采用适当的模糊隶属度来减少污染物输入产生的噪音。使用线性和非线性两个函数来制定两个不平行的超平面。 IFTSVM 不仅可以减少噪声的影响，还可以将噪声与支持向量区分开来。此外，这种修改可以最小化新制定的结构风险并提高分类准确性。采用两个人工问题和十一个基准问题来评估所提出的 IFTSVM 模型的有效性。为了对结果进行统计量化，使用了具有 95% 置信区间的引导技术。结果表明，与原始支持向量机、模糊支持向量机、FTSVM 和文献中报道的其他模型相比，IFTSVM 能够产生有希望的结果。