Title
Acquisition of characteristic sets of block preserving outerplanar graph patterns by a two-stage evolutionary learning method for graph pattern sets.
Abstract
Knowledge acquisition from graph structured data is an important task in machine learning and data mining. Block preserving outerplanar graph patterns are graph structured patterns having structured variables and are suited to represent characteristic graph structures of graph data modelled as outerplanar graphs. We propose a learning method for acquiring characteristic sets of block preserving outerplanar graph patterns by a two-stage evolutionary learning method for graph pattern sets as individuals, from positive and negative outerplanar graph data, in order to represent characteristic graph structures more concretely.
Year
DOI
Venue
2018
10.1504/IJCISTUDIES.2018.096191
IJCIStudies
Field
DocType
Volume
Graph,Outerplanar graph,Computer science,Genetic programming,Theoretical computer science,Graph structured data,Artificial intelligence,Evolutionary learning,Machine learning,Knowledge acquisition
Journal
7
Issue
Citations 
PageRank 
3/4
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Fumiya Tokuhara100.34
Tetsuhiro Miyahara226732.75
Tetsuji Kuboyama314029.36
Yusuke Suzuki415018.82
Tomoyuki Uchida525535.06