C4.5: programs for machine learning
C4.5: programs for machine learning
Towards situated knowledge acquisition
International Journal of Human-Computer Studies
Computer science as empirical inquiry: symbols and search
Communications of the ACM
Knowledge Acquisition without Analysis
Proceedings of the 7th European Workshop on Knowledge Acquisition for Knowledge-Based Systems
Knowledge in Context: A Strategy for Expert System Maintenance
AI '88 Proceedings of the 2nd Australian Joint Artificial Intelligence Conference
Validating knowledge acquisition: multiple classification ripple-down rules
Validating knowledge acquisition: multiple classification ripple-down rules
Incremental knowledge acquisition for search control heuristics
Incremental knowledge acquisition for search control heuristics
Epistemological Approach to the Process of Practice
Minds and Machines
The Ballarat incremental knowledge engine
PKAW'10 Proceedings of the 11th international conference on Knowledge management and acquisition for smart systems and services
Detection of CAN by ensemble classifiers based on ripple down rules
PKAW'12 Proceedings of the 12th Pacific Rim conference on Knowledge Management and Acquisition for Intelligent Systems
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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.