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首页> 外文期刊>Fuzzy Optimization and Decision Making: A Journal of Modeling and Computation Under Uncertainty >An intuitionistic fuzzy set based (SVM)-V-3 model for binary classification with mislabeled information
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An intuitionistic fuzzy set based (SVM)-V-3 model for binary classification with mislabeled information

机译：基于（SVM）-V-3模型的直觉模糊集，用于二进制分类，误标记信息

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摘要

Traditionally, robust and fuzzy support vector machine models are used to handle the binary classification problem with noise and outliers. These models in general suffer from the negative effects of having mislabeled training points and disregard position information. In this paper, we propose a novel method to better address these issues. First, we adopt the intuitionistic fuzzy set approach to detect suspectable mislabeled training points. Then we omit their labels but use their full position information to build a semi-supervised support vector machine model. After that, we reformulate the corresponding model into a non-convex problem and design a branch-and-bound algorithm to solve it. A new lower bound estimator is used to improve the accuracy and efficiency for binary classification. Numerical tests are conducted to compare the performances of the proposed method with other benchmark support vector machine models. The results strongly support the superior performance of the proposed method.
机译：传统上，鲁棒和模糊支持向量机模型用于处理噪声和异常值的二进制分类问题。这些模型一般遭受误标记培训点和忽视位置信息的负面影响。在本文中，我们提出了一种更好地解决这些问题的新方法。首先，我们采用直觉模糊集合方法来检测可疑误标标签培训点。然后我们省略了他们的标签，但使用他们的全部位置信息来构建半监督支持向量机模型。之后，我们将相应的模型重构为非凸面问题并设计分支和绑定算法来解决它。新的下限估计器用于提高二进制分类的准确性和效率。进行数值测试以比较所提出的方法与其他基准支持向量机模型的性能。结果强烈支持所提出的方法的卓越性能。

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