Classification and modeling with linguistic information granules - advanced approaches to linguistic data mining
By Hisao Ishibuchi, Tomoharu Nakashima, M. Nii. Advanced information processing, 2004
439 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/6965c8e2dcd6ecf2a5f3f9320de5fced37391e42
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Citations in the corpus
- A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems (2011, unknown): “In addition, fuzzy rule-based classification systems (FRBCSs) [94,107] consider a linguistic fuzzy rule structure where the output involves a discrete value, the class associated to the patterns matching the rule antecedent.”
- A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets (2008, methodology): “In this paper we study the performance of Fuzzy Rule Based Classification Systems (FRBCSs) [23] in the field of imbalanced data-sets.”
- A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning (2011, background): “FUZZY rule-based classification systems (FRBCSs) [1], [2] are useful and well-known tools in the machine learning framework, since they can provide an interpretable model for”
- Genetic fuzzy systems: taxonomy, current research trends and prospects (2008, background): “For more information about fuzzy systems the following books may be consulted [27, 64, 75, 108].”
- Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning (2007, background): “To determine the consequent class Cq and the rule weight CFq, we first calculate the confidence of the fuzzy rule ‘‘Aq) Class h’’ for each class h as follows (see the textbook on fuzzy data mining [25] for fuzzy versions of some basic concepts in data mining such as confidence and support):”