Research of new strategies for improving CBR system

Abstract

Case based reasoning (CBR), as an important AI technology, has gained popularity for its unique means of problem solving, which solves a new problem by remembering previous similar situations and reusing knowledge from the solutions to these situations. To construct a CBR system, two key issues have to be considered: one is feature selection, through which important features are extracted from the whole experience case and make up a case; the other is case retrieval, through which most appropriate case is retrieved for reuse. In order to further improve the accuracy of CBR system, this paper proposes a new feature selection method called Calculating Differences based on Growing Hierarchical Self Organizing Map clustering (CD-GHSOM) and a new case retrieval method called Growing Hierarchical Self Organizing Map based Case Retrieval (GHSOM-CR). Lots of experiments are implemented to validate the effectiveness of the proposed methods by comparing them with other recent researches.