An evidential integrated method for maintaining case base and vocabulary containers within CBR systems

Abstract

Cases and vocabulary maintenance presents a crucial task to preserve high competent Case-Based Reasoning (CBR) systems, since the accuracy of their offered solutions are strongly dependent on stored cases and their describing attributes quality. The maintenance aims generally at eliminating two types of undesirable knowledge which are noisy and redundant data. However, inexpedient Case Base Maintenance (CBM) or vocabulary maintenance may not only greatly decrease CBR competence in solving new problems, but also reduce its performance in term of retrieval time. Besides, to provide a high maintenance quality, it is necessary to manage uncertainty within knowledge since “real-world data are never perfect” and stored cases within a CBR system’s Case Base (CB) describe real-world experiences. Hence, we propose, in this paper, a new integrated method that maintains both of the CB and the vocabulary knowledge containers of CBR systems by offering a new alternating technique to properly detect noisiness and redundancy whether in cases or features. During the learning steps of our new integrated maintenance policy, which drives the decision making about cases and attributes selection, we manage uncertainty using one among the most powerful tools called the Belief Function Theory.