Learning taxonomic relation by case-based reasoning

Abstract

AbstractIn this paper, we propose a learning method of minimal casebase to represent taxonomic relation in a tree-structured concept hierarchy. We firstly propose case-based taxonomic reasoning and show an upper bound of necessary positive cases and negative cases to represent a relation. Then, we give a learning method of a minimal casebase with sampling and membership queries. We analyze this learning method by sample complexity and query complexity in the framework of PAC learning