Abstract
Label semantics is a random set framework for modelling with words. In previous work, several machine learning algorithms based on this framework have been proposed and studied. In this paper, we introduce a new linguistic rule induction algorithm based on Quinlan's FOIL algorithm. According to this algorithm, a set of linguistic rules is generated for classification problems. The new model is empirically tested on an artificial toy problem and several benchmark problems from UCI repository. The results show that the new model can generate very compact linguistic rules while maintaining comparable accuracy to other well-known data mining algorithms.
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