Acquisition of Characteristic Block Preserving Outerplanar Graph Patterns from Positive and Negative Data using Genetic Programming and Tree Representation of Graph Patterns
Acquisition of Characteristic Block Preserving Outerplanar Graph Patterns from Positive and Negative Data using Genetic Programming and Tree Representation of Graph Patterns
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使用遗传编程和图形模式的树表示从正负数据中获取保留外平面图形模式的特征块
DOI：
10.1109/iwcia.2015.7449469
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Fumiya Tokuhara
中科院分区：
文献类型：
--
作者：
Yuto Ouchiyama;Tetsuhiro Miyahara;Yusuke Suzuki;Tomoyuki Uchida;Tetsuji Kuboyama;Fumiya Tokuhara
基于图结构数据的机器学习和数据挖掘已经得到了广泛的研究。许多化合物可以用外平面图表示。我们使用具有结构变量的块保持外平面图模式来表示外平面图的结构特征。提出了一种利用遗传规划和块保持外平面图模式的树表示方法，从正、负外平面图数据中获取特征块保持外平面图模式的学习方法.我们报告的实验结果应用我们的方法合成外平面图形数据。
Machine learning and data mining from graph structured data have been studied intensively. Many chemical compounds can be expressed by outerplanar graphs. We use block preserving outerplanar graph patterns having structured variables for expressing structural features of outerplanar graphs. We propose a learning method for acquiring characteristic block preserving outerplanar graph patterns from positive and negative outerplanar graph data, by using Genetic Programming and tree representation of block preserving outerplanar graph patterns. We report experimental results on applying our method to synthetic outerplanar graph data.