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An incremental support vector machine-trained TS-type fuzzy system for online classification problems

机译：在线分类问题的增量支持向量机训练TS型模糊系统

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摘要

This paper proposes an incremental support vector machine-trained TS-type fuzzy classifier (ISVM-FC). The 1SVM-FC is a fuzzy system that consists of Takagi-Sugeno (TS)-type fuzzy rules. Structure and parameters in the ISVM-FC are trained incrementally from one subset of training data at a time. This incremental training approach avoids the use of large amounts of memory required for storing training data in batch learning, reduces training time, and adapts the classifier to time-dependent classification systems where training data are available sequentially. Initially, there are no fuzzy rules for structure learning with the ISVM-FC. It generates all rules according to the distribution of the training data. An incremental linear support vector machine (SVM) is used to tune the resulting rule parameters to give the classifier better generalization performance. The use of incremental learning discards past training data adaptively according to its distance to the linear hyperplane, thereby improving learning efficiency. Three simulations are conducted to verify the performance of the ISVM-FC. Comparisons with fuzzy classifiers and Gaussian-kernel SVM with batch and incremental learning modes demonstrate that the ISVM-FC improves training and test times, and reduces memory consumption for classifier storage without deteriorating the generalization ability.
机译：本文提出了一种增量支持向量机训练的TS型模糊分类器（ISVM-FC）。 1SVM-FC是由Takagi-Sugeno（TS）类型的模糊规则组成的模糊系统。一次从一个训练数据子集中对ISVM-FC中的结构和参数进行增量训练。这种渐进式训练方法避免了在批处理学习中存储训练数据所需的大量内存的使用，减少了训练时间，并使分类器适用于时间相关的分类系统，在该系统中顺序提供训练数据。最初，对于使用ISVM-FC进行结构学习没有模糊规则。它根据训练数据的分布生成所有规则。增量线性支持向量机（SVM）用于调整结果规则参数，以使分类器具有更好的泛化性能。增量学习的使用根据其到线性超平面的距离来自适应地丢弃过去的训练数据，从而提高学习效率。进行了三个仿真，以验证ISVM-FC的性能。通过将模糊分类器与具有批量和增量学习模式的高斯核SVM进行比较，可以证明ISVM-FC改善了训练和测试时间，并减少了分类器存储的内存消耗，而不会降低泛化能力。

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