論文

査読有り
2017年12月13日

Acquisition of multiple block preserving outerplanar graph patterns by an evolutionary method for graph pattern sets

2017 IEEE 10th International Workshop on Computational Intelligence and Applications, IWCIA 2017 - Proceedings
  • Fumiya Tokuhara
  • ,
  • Tetsuhiro Miyahara
  • ,
  • Tetsuji Kuboyama
  • ,
  • Yusuke Suzuki
  • ,
  • Tomoyuki Uchida

2017-
開始ページ
191
終了ページ
197
記述言語
英語
掲載種別
研究論文(国際会議プロシーディングス)
DOI
10.1109/IWCIA.2017.8203583
出版者・発行元
Institute of Electrical and Electronics Engineers Inc.

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 modeled as outerplanar graphs. We propose a learning method for acquiring characteristic multiple block preserving outerplanar graph patterns by evolutionary computation using graph pattern sets as individuals, from positive and negative outerplanar graph data, in order to represent characteristic graph structures more precisely.

リンク情報
DOI
https://doi.org/10.1109/IWCIA.2017.8203583
URL
http://dblp.uni-trier.de/db/conf/IEEEiwcia/iwcia2017.html#conf/IEEEiwcia/TokuharaMKSU17
ID情報
  • DOI : 10.1109/IWCIA.2017.8203583
  • DBLP ID : conf/IEEEiwcia/TokuharaMKSU17
  • SCOPUS ID : 85047218897

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