A hierarchical perspective to fuzzy inductive reasoning: an attempt to obtain more understandable fuzzy inductive reasoning rules
Document typeConference lecture
Rights accessRestricted access - publisher's policy
Generalizing hypotheses based on the past data in order to predict the future is the essential core of human learning. Various successful methods and techniques have been developed so far that perform some sort of classification of current data in order to predict future unseen cases. Multi class classification problems are among them as well. In many domains in spite of these automatic techniques, involvement of human experts is crucial. In this paper we are proposing a Hierarchical perspective to Fuzzy Inductive Reasoning (FIR) method as a classifier, in order to provide more insights for experts to the predictive model offered by FIR. Also, This method puts a hierarchical constrain on FIR's generalization which might be useful in finding and predicting exceptional cases of data that don't follow the general rule offered by the model.
CitationBagherpour, S., Mugica, F., Nebot, M. A hierarchical perspective to fuzzy inductive reasoning: an attempt to obtain more understandable fuzzy inductive reasoning rules. A: IEEE International Conference on Fuzzy Systems. "2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2015), August 2-5, 2015 Istanbul, Turkey". Estambul: 2015, p. 1-8.