Abstract:One of the perceived benefits of Case-Based Reasoning (CBR) is the potential to use retrieved cases to explain predictions. Surprisingly, this aspect of CBR has not been much researched. There has been some early work on knowledge-intensive approaches to CBR where the cases contain explanation patterns (e.g. SWALE). However, a more knowledge-light approach where the case similarity is the basis for explanation has received little attention. To explore this, we have developed a CBR system for predicting blood-alcohol level. We compare explanations of predictions produced with this system with alternative rule-based explanations. The casebased explanations fare very well in this evaluation and score significantly better than the rule-based alternative.

Abstract.In Wrapper based feature selection, the more states that are visited during the search phase of the algorithm the greater the likelihood of finding a feature subset that has a high internal accuracy while generalizing poorly. When this occurs, we say that the algorithm has overfitted to the training data. We outline a set of experiments to show this and we introduce a modified genetic algorithm to address this overfitting problem by stopping the search before overfitting occurs. This new algorithm called GAWES (Genetic Algorithm With Early Stopping) reduces the level of overfitting and yields feature subsets that have a better generalization accuracy.