Acquisition of Multiple Graph Structured Patterns by an Evolutionary Method Using Sets of TTSP Graph Patterns as Individuals
Yuuki Yamagata, Tetsuhiro Miyahara, Yusuke Suzuki, Tomoyuki Uchida, Fumiya Tokuhara, Tetsuji Kuboyama · 2017
Knowledge acquisition from graph structured data is an important task in machine learning and data mining. TTSP (Two-Terminal Series Parallel) graphs are used as data models for electric networks and scheduling. We propose a learning method for acquiring characteristic multiple graph structured patterns by evolutionary computation using sets of TTSP graph patterns as individuals, from positive and negative TTSP graph data, in order to represent sets of TTSP graphs more precisely.