A decision theoretic framework for approximating concepts
International Journal of Man-Machine Studies
C4.5: programs for machine learning
C4.5: programs for machine learning
Variable precision rough set model
Journal of Computer and System Sciences
From rough set theory to evidence theory
Advances in the Dempster-Shafer theory of evidence
Elements of machine learning
Information Sciences: an International Journal
Machine Learning
Automated Discovery of Plausible Rules Based on Rough Sets and Rough Inclusion
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
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In existing studies, diagnostic reasoning has been modeled as if-then rules in the literature. However, closer examinations suggests that medical diagnostic reasoning should consist of multiple strategies, in which one of the most important characteristics is that domain experts change the granularity of rules in a flexible way. First, medical experts use the coarsest information granules (as rules) to select the foci. For example, if the headache of a patient comes from vascular pain, we do not have to examine the possibility of muscle pain. Next, medical experts switches the finer granules to select the candidates. After several steps, they reach the final diagnosis by using the finest granules for this diagnostic reasoning. In this way, the coarseness or fineness of information granules play a crucial role in the reasoning steps. In this paper, we focus on the characteristics of this medical reasoning from the viewpoint of granular computing and formulate the strategy of switching the information granules. Furthermore, using the proposed model, we introduce an algorithm which induces if-then rules with a given level of granularity.