[1O3-GS-8] Soft Computing

[1O3-GS-8-02] Acquisition of Multiple Block Preserving Outerplanar Graph Patterns with Wildcards by Evolutionary Learning using Label Information

Fumiya Tokuhara¹, Shiho Okinaga¹, 〇Tetsuhiro Miyahara¹, Yusuke Suzuki¹, Tetsuji Kuboyama², Tomoyuki Uchida¹ (1. Hiroshima City University, 2. Gakushuin University)

Keywords:evolutionary learning, genetic programming, graph structured pattern

Machine learning from graph structured data are studied intensively.
Many chemical compounds can be expressed by outerplanar graphs.
The purpose of this paper is to propose a learning method for obtaining characteristic graph patterns from positive and negative outerplanar graph data.
We propose a two-stage evolutionary learning method for acquiring characteristic multiple block preserving outerplanar graph patterns with wildcards from positive and negative outerplanar graph data, by using label information of positive examples.
We report preliminary experimental results on our evolutionary learning method.