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Building Multiple Classifier Systems Using Linear Combinations of Reduced Graphs.

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This study introduces a novel graph classification framework using reduced graph subspaces and node centrality measures. Combining distances from these subspaces improves classification accuracy for general graphs.

Keywords:
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Area of Science:

  • Computer Science
  • Machine Learning
  • Graph Theory

Background:

  • General graph classification is challenging due to complex structures.
  • Standard pattern recognition methods often fail on graph data.
  • Existing methods like graph matching and kernel machines have limitations.

Purpose of the Study:

  • To propose a novel framework for general graph classification.
  • To leverage information from reduced graph subspaces for improved accuracy.
  • To address the limitations of current graph classification techniques.

Main Methods:

  • Generating reduced graphs using node centrality measures.
  • Computing graph edit distances within subspaces.
  • Combining distances using a linear combination for classification.

Main Results:

  • The proposed framework effectively classifies general graphs.
  • Utilizing multiple reduced graph subspaces enhances classification performance.
  • Empirical validation on six datasets confirms the system's benefits.

Conclusions:

  • The novel framework offers a promising approach to general graph classification.
  • Combining information from graph subspaces is beneficial.
  • The method demonstrates improved accuracy over existing techniques.