Abstract Multiple Support Vector Machine (SVM) classifier based on ensemble learning approaches could be enhanced from the view point of accuracy, but the performance of these classifiers closely depends on the initial condition of the partitioning method used in the design. Furthermore, these classifiers are more easily affected by noise and outliers. In this study, a novel fuzzy quasi-linear SVM classifier realized with the aid of a composite kernel function and Fuzzy C-Means (FCM) clustering is proposed. The objective of this approach is to reduce the effect of noise and outliers and also handle the overfitting problem through the synergistic effect of the two methods: First, Fuzzy C-Means (FCM) is used to partition the training dataset into several subsets as a preprocessing phase of the proposed classifier. Second, the composite kernel based on multiple linear kernel expression is considered to avoid overfitting problem. In more detail, each training data is assigned to the corresponding membership degree. Some data which are potential noise or outliers are assigned with lower membership degrees and thus yield a small contribution to the composite kernel function. Then, the composite kernel function for multiple local SVMs is constructed according to the distribution of training data. The designed fuzzy quasi-linear SVM classifier is tested with both artificial and UCI data sets. It is also applied for sorting the problem of black plastic wastes being handled in the practice in order to verify the effective as well as efficient classification improvement. Experimental results demonstrate that the proposed method shows the elevated classification performance when compared to performance produced by the methods studied previously.

中文翻译：

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模糊拟线性 SVM 分类器：设计与分析

摘要 基于集成学习方法的多支持向量机（SVM）分类器可以从准确性的角度来提高，但这些分类器的性能密切依赖于设计中使用的分区方法的初始条件。此外，这些分类器更容易​​受到噪声和异常值的影响。在这项研究中，提出了一种借助复合核函数和模糊 C 均值 (FCM) 聚类实现的新型模糊准线性 SVM 分类器。这种方法的目标是通过两种方法的协同效应来减少噪声和异常值的影响，并处理过拟合问题：首先，模糊 C 均值 (FCM) 用于将训练数据集划分为几个子集作为建议分类器的预处理阶段。第二，考虑基于多重线性核表达式的复合核以避免过拟合问题。更详细地说，每个训练数据都被分配到相应的隶属度。一些潜在噪声或异常值的数据被分配了较低的隶属度，因此对复合核函数的贡献很小。然后，根据训练数据的分布构造多个局部SVM的复合核函数。设计的模糊准线性 SVM 分类器通过人工和 UCI 数据集进行测试。也应用于实践中处理的黑色塑料垃圾分类问题，以验证分类改进的有效性和效率。