Induction of meta-knowledge about knowledge discovery

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Abstract
A study is reported of the use of ripple-down rule induction to develop a meta-model of ten years of clinical data captured as part of the development of an expert system for thyroid diagnosis. The study shows how the suitability for inductive knowledge discovery of such real-world data can be characterized in terms of its stationarity, and how the best error rates achievable and the amount of data necessary to achieve them, can be estimated.