Generalising symbolic knowledge in online classification and prediction

Increasingly, researchers and developers of knowledge based systems (KBS) have been incorporating the notion of context. For instance, Repertory Grids, Formal Concept Analysis (FCA) and Ripple-Down Rules (RDR) all integrate either implicit or explicit contextual information. However, these methodologies treat context as a static entity, neglecting many connectionists' work in learning hidden and dynamic contexts, which aid their ability to generalize. This paper presents a method that models hidden context within a symbolic domain in order to achieve a level of generalisation. The method developed builds on the already established Multiple Classification Ripple-Down Rules (MCRDR) approach and is referred to as Rated MCRDR (RM). RM retains a symbolic core, while using a connection based approach to learn a deeper understanding of the captured knowledge. This method is applied to a number of classification and prediction environments and results indicate that the method can learn the information that experts have difficulty providing. © Springer-Verlag Berlin Heidelberg 2009