Acquisition of Multiple Tree Structured Patterns by an Evolutionary Method Using Sets of Tag Tree Patterns as Individuals

Abstract

Knowledge acquisition from tree structured data is an important task in machine learning and data mining. A tag tree pattern is a rooted tree structured pattern which has ordered children and structured variables representing arbitrary sub tree structures. In order to represent tree structured data about complex phenomena, we propose a learning method for acquiring characteristic multiple tree structured patterns by evolutionary computation using sets of tag tree patterns as individuals, from positive and negative tree structured data.