DETD: Dynamic policy for case base maintenance based on EK-NNclus algorithm and case Types Detection

International audienceCase Based Reasoning (CBR) systems know a success in various domains. Consequently, we find several works focusing on Case Base Maintenance (CBM) that aim to preserve CBR systems performance. Thus, CBM tools are generally offering techniques to select only the most potential cases for problem-solving. However, cases are full of imperfection since they represent real world situations, which makes this task harder. In addition, new problems having substantially new solutions will be found in case bases over the time. Hence, we aim, in this paper, to propose a new CBM approach having the ability to manage uncertainty and the dynamic aspect of maintenance using the evidential clustering technique called EK-NNclus based on belief function theory, where clusters' number is fixed automatically and changes from one maintenance application to another. Finally, the maintenance task is performed through selecting only two types of cases