A description length-based decision criterion for default knowledge in the ripple down rules method
Knowledge and Information Systems
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Two decades of ripple down rules research
The Knowledge Engineering Review
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Ripple Down Rules (RDR) Method is an incremental Knowledge Acquisition (KA) approach that is able to capture human expertise efficiently. The expert's KA tasks in RDR are 1) to identify the correct class label of each misclassified case and 2) to select important attributes that distinguish the misclassified case from the previous correctly classified case. The latter task is more difficult than the former one since it requires much thought on human expert. This paper proposes a method for reducing the task on human expert by generating context-bounded cases and utilizing them to replace the latter task with the former one. Experiments on the datasets from UCI were carried out to evaluate the proposed method and the result confirmed that it is effective and as good as the standard RDR method on most datasets.