Integrating Inductive Learning to the Ripple Down Rules Method with the Minimum Description Length Principle

Abstract

A Knowledge Acquisition method “Ripple Down Rules” can directly acquire and encode knowledge from human experts. It is an incremental acquisition method and each new piece of knowledge is added as an exception to the existing knowledge base. This knowledge base takes the form of a binary tree. There is another type of knowledge acquisition method that learns directly from data. Induction of decision tree is one such representative example. Noting that more data are stored in the database in this digital era, use of both expertise of humans and these stored data becomes even more important. In this paper, we attempt to integrate inductive learning and knowledge acquisition. We show that using the minimum description length principle, the knowledge base of Ripple Down Rules is automatically and incrementally constructed from data and thus, making it possible to switch between manual acquisition by a human expert and automatic induction from data at any point of knowledge acquisition. Experiments are carefully designed and tested to verify that the proposed method indeed works for many data sets having different natures.