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by Stan Matwin, Riverson Rios, Jim Mount
http://www.csi.uottawa.ca/~riverson/publ/idamap96.ps.gz
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Abstract:
Abstract. In many medical domains in which one is to apply Artificial Intelligence-based methods for the analysis of available data, two entities exist: the domain theory and the data. Traditional machine learning systems tend to almost completely disregard the former. In medical applications, the experts will be reluctant to accept any results of the induction process unless they can be interpreted using their mental model of the domain. We argue that explainable induction will remedy this difficulty, by learning rules which can be justified (explained) in terms of a pre-coded domain theory. We present an algorithm to explain a set of inductively-derived rules from an expert-provided model, and we show how we apply this approach in learning rules for the diagnosis of retinal diseases. 1
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