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Fuzzy quasi-linear SVM classifier: Design and analysis

机译：模糊拟线性SVM分类器：设计与分析

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摘要

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. ? 2020 Elsevier B.V. All rights reserved.
机译：可以从观点的准确度提高基于集合学习方法的多个支持向量机（SVM）分类器，但这些分类器的性能非常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。在该研究中，提出了一种借助于复合核函数和模糊C-MATION（FCM）聚类实现的新型模糊准线性SVM分类器。这种方法的目的是降低噪声和异常值的效果，并通过两种方法的协同效果来处理过度拟合问题：首先，模糊C-mancy（FCM）用于将训练数据集分为几个子集作为一个所提出的分类器的预处理阶段。其次，基于多线性内核表达式的复合内核被认为是避免过量的问题。更详细地，每个训练数据被分配给相应的隶属度。有些数据是潜在的噪声或异常值的分配，较低的隶属度，从而为复合内核功能产生了很小的贡献。然后，根据训练数据的分布来构建多个本地SVMS的复合内核功能。使用人工和UCI数据集进行设计的模糊准线性SVM分类器。它还用于分类在实践中处理的黑色塑料废物问题，以验证有效的和有效的分类改进。实验结果表明，与先前研究的方法产生的性能相比，所提出的方法显示出升高的分类性能。还2020 Elsevier B.V.保留所有权利。

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  • 来源
    《Fuzzy sets and systems》 |2021年第15期|42-63|共22页
  • 作者

    Yang Cheng; Oh Sung-Kwun; Yang Bo; Pedrycz Witold; Fu ZunWei;

  • 作者单位

    Univ Suwon Dept Comp Engn San 2-2 Wau Ri Hwaseong Si 445743 Gyeonggi Do South Korea;

    Linyi Univ Res Ctr Big Data & Artificial Intelligence Linyi 276005 Shandong Peoples R China|Univ Suwon Sch Elect & Elect Engn Hwaseong Si 18323 Gyeonggi Do South Korea;

    Linyi Univ Sch Informat Sci & Engn Linyi 276005 Shandong Peoples R China;

    Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada|King Abdulaziz Univ Dept Elect & Comp Engn Fac Engn Jeddah 21589 Saudi Arabia|Polish Acad Sci Syst Res Inst Warsaw Poland;

    Linyi Univ Res Ctr Big Data & Artificial Intelligence Linyi 276005 Shandong Peoples R China|Qufu Normal Univ Sch Math Sci Qufu 273100 Shandong Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    SVM classifier; Composite kernel function; Membership degrees; Black plastic wastes sorting; Particle Swarm Optimization (PSO);

    机译：SVM分类器;复合内核功能;会员度;黑色塑料废物分类;粒子群优化（PSO）;

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