Applying CBR Principles to Reason without Negative Exemplars

We investigate a method for applying CBR to a source of data where there are no negative exemplars. Our problem domain is one of recommending characteristics of multidisciplinary collaborators based on a collection of funded grants. Thus, there are no negative exemplars. Lacking sufficient domain knowledge, we seek to apply a feedback algorithm to learn weights even in the absence of negative exemplars. Our approach is based on the assumption that well aligned cases, cases where similar problems have similar solutions, are better suited for learning feature weights. Our approach clusters the problem and solution spaces separately to identify well aligned cases. We also identify poorly aligned cases that may hinder effective learning of weights, and exclude them. The clusters of well aligned cases provide a means to utilize feedback algorithms. We use two methods, case alignment and case cohesion, to show that our approach succeeds in identifying well aligned cases. We also compare our approach to a method based on single class learning, a machine learning approach for reasoning without negatives. Our results show that our approach is viable to learning weight in the absence of negative exemplars.