A framework of features selection for the case-based reasoning

Abstract

CBR is a problem solving technique that reuses past cases and experiences to find a solution to the problems. A critical issue in case-based reasoning is to select the correct and enough features to represent a case. For this reason, the analysis of cases and extraction the necessary features to represent a case are highly recommended in building a CBR system. However, this task is difficult to carry out since such knowledge often cannot be successfully and exhaustively captured and represented. In this paper, a framework of features mining system for the case-based reasoning including two phases has been proposed. The techniques of features selection, data analysis and machine learning can thus be effectively integrated. This will promote flexibility and expandability of case-based reasoning system.