Using canonical representations of block tree patterns in acquisition of characteristic block preserving outerplanar graph patterns
Fumiya Tokuhara, Tetsuhiro Miyahara, Yusuke Suzuki, Tomoyuki Uchida, Tetsuji Kuboyama · 2016
We consider evolutionary learning, based on Genetic Programming, for acquiring characteristic graph structures from positive and negative outerplanar graph data. We use block preserving outerplanar graph patterns as representations of graph structures. Block tree patterns are tree representations of block preserving outerplanar patterns, and have the structure of unrooted trees some of whose vertices have ordered adjacent vertices. In this paper we propose canonical representations, which are representations having the structure of rooted and ordered trees, of block tree patterns in acquiring characteristic block preserving outerplanar graph patterns. Then we give an algorithm for calculating canonical representations of block tree patterns. Preliminary experimental results show the algorithm is effective in reducing the run time of our evolutionary learning method.