Tetsuji Kuboyama

Machine learning and data mining from tree structured data are studied intensively. We propose an evolutionary learning method for acquiring characteristic tag tree patterns with vertex labels and wildcards from positive and negative tree data, by using label information of positive examples. We report preliminary experimental results on our evolutionary learning method.

Machine learning from graph structured data are studied intensively. Many chemical compounds can be expressed by outerplanar graphs. The purpose of this paper is to propose a learning method for obtaining characteristic graph patterns from positive and negative outerplanar graph data. We propose a two-stage evolutionary learning method for acquiring characteristic multiple block preserving outerplanar graph patterns with wildcards from positive and negative outerplanar graph data, by using label information of positive examples. We report preliminary experimental results on our evolutionary learning method.