Evolution of Multiple Tree Structured Patterns from Tree-Structured Data Using Clustering

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@InProceedings{DBLP:conf/ausai/NagamineMKUT08,

• author = "Masatoshi Nagamine and Tetsuhiro Miyahara and Tetsuji Kuboyama and Hiroaki Ueda and Kenichi Takahashi",
• title = "Evolution of Multiple Tree Structured Patterns from Tree-Structured Data Using Clustering",
• editor = "Wayne Wobcke and Mengjie Zhang",
• booktitle = "AI 2008: 21st Australasian Joint Conference on Artificial Intelligence",
• publisher = "Springer",
• series = "Lecture Notes in Computer Science",
• volume = "5360",
• year = "2008",
• pages = "500--511",
• bibsource = "DBLP, http://dblp.uni-trier.de",
• address = "Auckland, New Zealand",
• month = dec # " 1-5",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-3-540-89377-6",
• DOI = "doi:10.1007/978-3-540-89378-3_51",
• abstract = "We propose a new genetic programming approach to extraction of multiple tree structured patterns from tree-structured data using clustering. As a combined pattern we use a set of tree structured patterns, called tag tree patterns. A structured variable in a tag tree pattern can be substituted by an arbitrary tree. A set of tag tree patterns matches a tree, if at least one of the set of patterns matches the tree. By clustering positive data and running GP subprocesses on each cluster with negative data, we make a combined pattern which consists of best individuals in GP subprocesses. The experiments on some glycan data show that our proposed method has a higher support of about 0.8 while the previous method for evolving single patterns has a lower support of about 0.5.",

}

Genetic Programming entries for Masatoshi Nagamine Tetsuhiro Miyahara Tetsuji Kuboyama Hiroaki Ueda Ken-ichi Takahashi

Citations