In recent years, twin support vector regression has become a hot research topic because of its low computing time and excellent performance. It can be observed, however, that either the support vector regression or twin support vector regression have no more than two regression hyperplanes. Many research studies have ignored the potential of multiple hyperplanes regression algorithms. In this paper, a one-versus-all twin support vector regression (OVATWSVR) with multiple regression hyperplanes is proposed, in order to achieve excellent regression performance though multi-hyperplane structure. Suppose that the input data implicitly has p categories, OVATWSVR solves a smaller quadratic programming problem (QPP) and repeats this process p times, resulting in p regression hyperplanes. For the purpose of mining the implicit category information of each point to assist OVATWSVR in training hyperplanes, meanwhile considering the fuzzy characteristics of points (they lack classification labels) in different types of regression datasets, we further propose a fuzzy clustering algorithm, namely fuzzy weighted K-nearest neighbors fuzzy density peak clustering (FKNN-FDPC), to provide OVATWSVR with information regarding the category of each point. A fuzzy membership function, also guided by FKNN-FDPC, is added to OVATWSVR in order to enhance the capability of OVATWSVR to handle possible fuzzy properties in data, thus creating FOVATWSVR. F3OVATWSVR is a reasonable name for the entire multiple phases’ algorithm. Several UCI benchmark datasets, a real-world competition dataset and a state of health (SOH) estimation of lithium-ion batteries dataset are used to verify the superiority and effectiveness of F3OVATWSVR.

中文翻译：

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模糊聚类引导的多超平面孪生支持向量回归

近年来，孪生支持向量回归因其计算时间短、性能优异而成为研究热点。然而，可以观察到，支持向量回归或孪生支持向量回归具有不超过两个回归超平面。许多研究都忽略了多超平面回归算法的潜力。本文提出了一种具有多个回归超平面的一对全孪生支持向量回归（OVATWSVR），以便通过多超平面结构实现优异的回归性能。假设输入数据隐式有 p 个类别，OVATWSVR 解决较小的二次规划问题 (QPP) 并重复此过程 p 次，产生 p 个回归超平面。为了挖掘每个点的隐含类别信息以辅助OVATWSVR训练超平面，同时考虑到不同类型回归数据集中点的模糊特征（它们缺乏分类标签），我们进一步提出了一种模糊聚类算法，即模糊加权K 最近邻模糊密度峰值聚类 (FKNN-FDPC)，为 OVATWSVR 提供有关每个点类别的信息。同样以FKNN-FDPC为指导的模糊隶属函数被添加到OVATWSVR中，以增强OVATWSVR处理数据中可能的模糊属性的能力，从而创建FOVATWSVR。 F3OVATWSVR 是整个多阶段算法的合理名称。使用多个UCI基准数据集、真实世界竞赛数据集和锂离子电池健康状态（SOH）估计数据集来验证F3OVATWSVR的优越性和有效性。