Detecting change via competence model

In real world applications, interested concepts are more likely to change rather than remain stable, which is known as concept drift. This situation causes problems on predictions for many learning algorithms including case-base reasoning (CBR). When learning under concept drift, a critical issue is to identify and determine "when" and "how" the concept changes. In this paper, we developed a competence-based empirical distance between case chunks and then proposed a change detection method based on it. As a main contribution of our work, the change detection method provides an approach to measure the distribution change of cases of an infinite domain through finite samples and requires no prior knowledge about the case distribution, which makes it more practical in real world applications. Also, different from many other change detection methods, we not only detect the change of concepts but also quantify and describe this change. © 2010 Springer-Verlag

Maintaining Footprint-Based Retrieval for Case Deletion

The effectiveness and efficiency of case-based reasoning (CBR) systems depend largely on the success of case-based retrieval. The case-base maintenance (CBM) issues become imperative and important especially for modern societies. This paper proposes a new competence model and a new maintenance procedure for the proposed competence model. Based on the proposed competence maintenance procedure, footprint-based retrieval (FBR), a competence-based case base retrieval method, is able to preserve its own retrieval effectiveness and efficiency. © 2009 Springer-Verlag Berlin Heidelberg