Zhu Zixuan, Zhao Yuhai
College of Computer Science and Engineering, Northeastern University, Shenyang 110819, China.
Entropy (Basel). 2018 Apr 2;20(4):245. doi: 10.3390/e20040245.
Recently, was proposed as the extension of and has achieved some successes. However, to the best of our knowledge, currently, there is no study working on , where each object is represented as a bag containing a number of graphs and each bag is marked with multiple class labels. It is an interesting problem existing in many applications, such as image classification, medicinal analysis and so on. In this paper, we propose an innovate algorithm to address the problem. Firstly, it uses more precise structures, multiple , instead of to represent an image so that the classification accuracy could be improved. Then, it uses multiple labels as the output to eliminate the semantic ambiguity of the image. Furthermore, it calculates the entropy to mine the informative subgraphs instead of just mining the frequent subgraphs, which enables selecting the more accurate features for the classification. Lastly, since the current algorithms cannot directly deal with graph-structures, we degenerate the into the in order to solve it by . The performance study shows that our algorithm outperforms the competitors in terms of both effectiveness and efficiency.
最近， 被提议作为 的扩展并已取得一些成功。然而，据我们所知，目前尚无针对 的研究，其中每个对象都表示为一个包含多个图的包，并且每个包都标记有多个类别标签。这是许多应用中存在的一个有趣问题，如图像分类、药物分析等。在本文中，我们提出了一种创新算法来解决该问题。首先，它使用更精确的结构，多个 ，而不是 来表示图像，从而提高分类精度。然后，它使用多个标签作为输出以消除图像的语义模糊性。此外，它计算熵以挖掘信息子图，而不仅仅是挖掘频繁子图，这使得能够为分类选择更准确的特征。最后，由于当前算法不能直接处理图结构，我们将 退化为 以便通过 来解决它。性能研究表明，我们的算法在有效性和效率方面均优于竞争对手。
