Support vector machine is a powerful technique for classification and regression problems. In the binary data problems, it classifies the points by assigning them to one of the two disjoint halfspaces. However, this method fails to handle the noises and outliers present in the dataset and the solution of a large-sized quadratic programming problem is required to obtain the decision surface in input or in feature space. We propose the intuitionistic fuzzy proximal support vector machine (IFPSVM) which classifies the patterns according to its proximity with the two parallel planes that are kept as distant as possible from each other. These two parallel ‘proximal’ planes can be obtained by solving a system of linear equations only. There is an intuitionistic fuzzy number associated with each training point which is framed by its degree of membership and non-membership. The membership degree of a pattern considers its distance from the corresponding class center and the degree of non-membership of a pattern is given by the ratio of the number of heterogeneous points to the number of total points in its neighborhood. The proposed technique effectively reduces the impact of noises and distinguishes the edge support vectors and outliers. Computational simulations on an artificial and eleven UCI benchmark datasets using linear, polynomial and Gaussian kernel functions, show the effectiveness of the proposed IFPSVM method. The experiments prove that it can handle large datasets with less computational time and yields better accuracy.

中文翻译：

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直觉模糊近邻支持向量机的模式分类

支持向量机是用于分类和回归问题的强大技术。在二进制数据问题中，它通过将点分配给两个不相交的半空间之一来对这些点进行分类。但是，该方法无法处理数据集中存在的噪声和离群值，并且需要大型二次规划问题的解决方案才能获得输入或特征空间中的决策面。我们提出了一种直观的模糊近端支持向量机（IFPSVM），它根据模式与两个平行平面的接近程度对模式进行分类，两个平行平面彼此保持尽可能远的距离。这两个平行的“近端”平面只能通过求解线性方程组来获得。每个训练点都有一个直观的模糊数，该模糊数由其隶属度和非隶属度构成。模式的隶属度考虑其与相应类中心的距离，并且模式的非隶属度由其异构点数与附近的总点数之比得出。所提出的技术有效地减少了噪声的影响并区分了边缘支持向量和离群值。使用线性，多项式和高斯核函数对人工和11个UCI基准数据集进行的计算仿真显示了所提出的IFPSVM方法的有效性。实验证明，该算法能够以较少的计算时间处理大型数据集，并且具有更高的准确性。模式的隶属度考虑其与相应类中心的距离，并且模式的非隶属度由其异构点数与附近的总点数之比得出。所提出的技术有效地减少了噪声的影响并区分了边缘支持向量和离群值。使用线性，多项式和高斯核函数对人工和11个UCI基准数据集进行的计算仿真显示了所提出的IFPSVM方法的有效性。实验证明，该算法能够以较少的计算时间处理大型数据集，并且具有更高的准确性。模式的隶属度考虑其与相应类中心的距离，并且模式的非隶属度由其异构点数与附近的总点数之比得出。所提出的技术有效地减少了噪声的影响并区分了边缘支持向量和离群值。使用线性，多项式和高斯核函数对人工和11个UCI基准数据集进行的计算仿真显示了所提出的IFPSVM方法的有效性。实验证明，该算法能够以较少的计算时间处理大型数据集，并且具有更高的准确性。