论文标题
使用标签噪声的数学编程方法用于二进制监督分类
A Mathematical Programming approach to Binary Supervised Classification with Label Noise
论文作者
论文摘要
在本文中，我们提出了新的方法，以构建支持向量机的分类器，这些分类器考虑了训练样本中出现标签噪声的基于支持的分类器。我们通过在训练数据集中重新标记一些观察结果的决策来基于解决混合整数线性和非线性模型的不同替代方案。第一种方法将重新标记直接包含在SVM模型中，而第二种方法则将聚类与分类同时结合在一起，从而产生了同时应用相似性度量和SVM的模型。根据从UCI机器学习存储库中获取的一系列标准数据集进行了广泛的计算实验，显示了所提出的方法的有效性。
In this paper we propose novel methodologies to construct Support Vector Machine -based classifiers that takes into account that label noises occur in the training sample. We propose different alternatives based on solving Mixed Integer Linear and Non Linear models by incorporating decisions on relabeling some of the observations in the training dataset. The first method incorporates relabeling directly in the SVM model while a second family of methods combines clustering with classification at the same time, giving rise to a model that applies simultaneously similarity measures and SVM. Extensive computational experiments are reported based on a battery of standard datasets taken from UCI Machine Learning repository, showing the effectiveness of the proposed approaches.
