Abstract In this study, we proposed combined kernel-based methods to leverage patent citation graph performance for patent classification. The concept is to use the combined graph kernels of the citation graph to classify patent documents, as a hybrid approach. A multiple kernel framework was used for integrating multiple datasets of various kernels into a combined kernel. We employed seven graph kernels as the baselines and the combination of random walks and Weisfeiler–Lehman subtree kernels to achieve higher performance. We calculated the kernel values of each patent pairwise and employed an SVM classifier to carry out the classification task. The investigation results demonstrate that the combined graph kernel outperforms single kernels.

中文翻译：

---

用于自动专利分类的组合图内核：一种混合方法

摘要 在这项研究中，我们提出了基于内核的组合方法，以利用专利引文图的性能进行专利分类。这个概念是使用引文图的组合图内核来对专利文件进行分类，作为一种混合方法。多内核框架用于将各种内核的多个数据集集成到一个组合内核中。我们采用七个图内核作为基线，并结合随机游走和 Weisfeiler-Lehman 子树内核来实现更高的性能。我们成对计算每个专利的核值，并采用 SVM 分类器来执行分类任务。调查结果表明，组合图内核优于单个内核。