Case-Based Reasoning and the Statistical Challenges II

Abstract

Case-based reasoning (CBR) solves problems using the already stored knowledge, and captures new knowledge, making it immediately available for solving the next problem. Therefore, CBR can be seen as a method for problem solving, and also as a method to capture new experience and make it immediately available for problem solving. The CBR paradigm has been originally introduced by the cognitive science community. The CBR community aims to develop computer models that follow this cognitive process. Up to now many successful computer systems have been established on the CBR paradigm for a wide range of real-world problems. We will review in this paper the CBR process and the main topics within the CBR work. Hereby we try bridging between the concepts developed within the CBR community and the statistics community. The CBR topics we describe are: similarity, memory organization,CBR learning, and case-base maintenance. The incremental aspect arising with the CBR paradigm will be considered as well as the life-time aspect of a CBR system. We will point out open problems within CBR that need to be solved. Finally we show on application how the CBR paradigm can be applied. The applications we are focusing on are meta-learning for parameter selection in technical systems, image interpretation, incremental prototype-based classification and novelty detection and handling. Finally, we summarize our concept on CBR.Keywordscase-based reasoningstatisticssimilarityincremental learningnormalizationcase base organizationnovelty detection