Hybrid case-base maintenance approach for modeling large scale case-based reasoning systems

Abstract

Abstract Case-based reasoning (CBR) is a nature inspired paradigm of machine learning capable to continuously learn from the past experience. Each newly solved problem and its corresponding solution is retained in its central knowledge repository called case-base. Withρ the regular use of the CBR system, the case-base cardinality keeps on growing. It results into performance bottleneck as the number of comparisons of each new problem with the existing problems also increases with the case-base growth. To address this performance bottleneck, different case-base maintenance (CBM) strategies are used so that the growth of the case-base is controlled without compromising on the utility of knowledge maintained in the case-base. This research work presents a hybrid case-base maintenance approach which equally utilizes the benefits of case addition as well as case deletion strategies to maintain the case-base in online and offline modes respectively. The proposed maintenance method has been evaluated using a simulated model of autonomic forest fire application and its performance has been compared with the existing approaches on a large case-base of the simulated case study.