Using Label Information in a Genetic Programming Based Method for Acquiring Block Preserving Outerplanar Graph Patterns with Wildcards
Using Label Information in a Genetic Programming Based Method for Acquiring Block Preserving Outerplanar Graph Patterns with Wildcards
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在基于遗传编程的方法中使用标签信息获取带通配符的块保留外平面图模式
DOI：
10.1109/iwcia47330.2019.8955031
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Uchida Tomoyuki
中科院分区：
文献类型：
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作者：
Tokuhara Fumiya;Okinaga Shiho;Miyahara Tetsuhiro;Suzuki Yusuke;Kuboyama Tetsuji;Uchida Tomoyuki
基于图结构数据的机器学习和数据挖掘得到了广泛的关注。许多化合物可以用外平面图表示。提出了一种利用正例的标签连接信息，通过遗传程序设计，从正、负外平面图数据中获取带通配符的特征块保持外平面图模式的方法.我们报告了真实的化合物数据和合成数据的实验结果。
Machine learning and data mining from graph structured data have gained much attention. Many chemical compounds can be expressed by outerplanar graphs. We propose a method for acquiring characteristic block preserving outerplanar graph patterns with wildcards for vertex and edge labels, from positive and negative outerplanar graph data, by Genetic Programming using label connecting information of positive examples. We report experimental results on real chemical compound data and synthetic data.