In this paper, we present an incremental variant of the Twin Support Vector Machine (TWSVM) called Fuzzy Bounded Twin Support Vector Machine (FBTWSVM) to deal with large datasets and to learn from data streams. We combine the TWSVM with a fuzzy membership function, so that each input has a different contribution to each hyperplane in a binary classifier. To solve the pair of quadratic programming problems (QPPs), we use a dual coordinate descent algorithm with a shrinking strategy, and to obtain a robust classification with a fast training we propose the use of a Fourier Gaussian approximation function with our linear FBTWSVM. Inspired by the shrinking technique, the incremental algorithm re-utilizes part of the training method with some heuristics, while the decremental procedure is based on a scoring window. The FBTWSVM is also extended for multi-class problems by combining binary classifiers using a Directed Acyclic Graph (DAG) approach. Moreover, we analyzed the theoretical foundation’s properties of the proposed approach and its extension, and the experimental results on benchmark datasets indicate that the FBTWSVM has a fast training and retraining process while maintaining a robust classification performance.

在本文中，我们介绍了称为模糊有界双支持向量机（FBTWSVM）的双支持向量机（TWSVM）的增量变体，用于处理大型数据集并从数据流中学习。我们将TWSVM与模糊隶属度函数相结合，以便每个输入对二进制分类器中的每个超平面都有不同的贡献。为了解决一对二次规划问题（QPP），我们使用具有缩减策略的双坐标下降算法，并且为了通过快速训练获得鲁棒的分类，我们建议对线性FBTWSVM使用傅里叶高斯逼近函数。受收缩技术的启发，增量算法通过一些启发式方法重新利用了一部分训练方法，而递减过程则基于计分窗口。通过使用有向无环图（DAG）方法组合二进制分类器，FBTWSVM也扩展为解决多类问题。此外，我们分析了该方法的理论基础及其扩展性，在基准数据集上的实验结果表明，FBTWSVM具有快速的训练和再训练过程，同时保持了强大的分类性能。