Abstract: Since the fuzzy twin support vector machine（FTSVM） algorithm is still sensitive to noise and prone to over fitting, as well as cannot effectively distinguish support vectors from outliers. This paper proposes an improved robust fuzzy twin support vector machine（IRFTSVM）. Firstly, a new kind of mixed membership function is constructed by combining the intra-class hyperplanemembership function and the improved [k]-nearest neighbor membership function. Secondly, a regularization term and the additional constraint are brought into the objective function to minimize the structural risk, avoid the computationof inverse matrix, and nonlinear problems can be directed from linear case as the classical SVM algorithm. Finally, the hinge loss function is replaced by the pinball loss function to reduce the noise sensitivity. In addition, the proposed algorithm is assessed and compared with SVM, TWSVM, FTSVM, PTSVM and TBSVM on some UCI datasets and an artificial dataset. The experimental results show that the proposed algorithm is satisfactory.

Key words: fuzzy twin support vector machine（FTSVM）, pinball loss function, hinge loss function, mixed membership function

摘要： 针对模糊孪生支持向量机算法（FTSVM）对噪声仍然敏感，容易过拟合以及不能有效区分支持向量和离群值等问题，提出了一种改进的鲁棒模糊孪生支持向量机算法（IRFTSVM）。将改进的[k]近邻隶属度函数和基于类内超平面的隶属度函数结合，构造了一种新的混合隶属度函数；在FTSVM算法的目标函数中引入正则化项和额外的约束条件，实现了结构风险最小化，避免了逆矩阵运算，且非线性问题可以像经典的SVM算法一样直接从线性问题扩展而来；将铰链损失函数替换为pinball损失函数，以此降低对噪声的敏感性。此外，在UCI数据集和人工数据集上对该算法进行评估，并与SVM、TWSVM、FTSVM、PTSVM和TBSVM五个算法进行比较。实验结果表明，该算法的分类结果是令人满意的。

关键词: 模糊孪生支持向量机算法（FTSVM）, pinball损失函数, 铰链损失函数, 混合隶属度函数