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Selecting Structural Base Classifiers for Graph-Based Multiple Classifier Systems

机译:为基于图的多个分类器系统选择结构基础分类器

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摘要

Selecting a set of good and diverse base classifiers is essential for building multiple classifier systems. However, almost all commonly used procedures for selecting such base classifiers cannot be directly applied to select structural base classifiers. The main reason is that structural data cannot be represented in a vector space. For graph-based multiple classifier systems, only using subgraphs for building structural base classifiers has been considered so far. However, in theory, a full graph preserves more information than its subgraphs. Therefore, in this work, we propose a different procedure which can transform a labelled graph into a new set of unlabelled graphs and preserve all the linkages at the same time. By embedding the label information into edges, we can further ignore the labels. By assigning weights to the edges according to the labels of their linked nodes, the strengths of the connections are altered, but the topology of the graph as a whole is preserved. Since it is very difficult to embed graphs into a vector space, graphs are usually classified based on pairwise graph distances. We adopt the dissimilarity representation and build the structural base classifiers based on labels in the dissimilarity space. By combining these structural base classifiers, we can solve the labelled graph classification problem with a multiple classifier system. The performance of using the subgraphs and full graphs to build multiple classifier systems is compared in a number of experiments.
机译:选择一组良好且多样化的基础分类器对于构建多个分类器系统至关重要。但是,几乎所有用于选择这种基础分类器的常用程序都不能直接应用于选择结构基础分类器。主要原因是结构数据无法在向量空间中表示。到目前为止,对于基于图的多重分类器系统,仅考虑使用子图来构建结构基础分类器。但是,从理论上讲,完整图比其子图保留更多的信息。因此,在这项工作中,我们提出了一个不同的过程,该过程可以将标记的图转换为一组新的未标记的图,并同时保留所有链接。通过将标签信息嵌入边缘,我们可以进一步忽略标签。通过根据其链接节点的标签为边缘分配权重,可以更改连接的强度,但可以保留整个图的拓扑。由于将图嵌入向量空间非常困难,因此通常基于成对图距离对图进行分类。我们采用差异表示,并基于差异空间中的标签构建结构基础分类器。通过组合这些结构基础分类器,我们可以使用多重分类器系统解决标记图分类问题。在许多实验中,比较了使用子图和完整图来构建多个分类器系统的性能。

著录项

  • 来源
    《Multiple classifier systems》|2010年|p.155-164|共10页
  • 会议地点 Cairo(EG);Cairo(EG);Cairo(EG)
  • 作者单位

    Pattern Recognition Laboratory, Delft University of Technology, The Netherlands;

    Pattern Recognition Laboratory, Delft University of Technology, The Netherlands;

    Institute of Computer Science and Applied Mathematics, University of Bern, Switzerland;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 TP274.3;
  • 关键词

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