Fuzzy Classifier Design

By Ludmila I. Kuncheva. Studies in Fuzziness and Soft Computing, 2000

489 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/686f827f8e61bdb49ba2e0cd4f5587b2dbda5f45

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In Artificial Intelligence

Citations in the corpus

Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] (2016, unknown): “6) Fuzzy Ensemble: A fuzzy classifier is the one that uses fuzzy sets or fuzzy logic in the course of its training or operation [38].”
A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning (2011, background): “F UZZY 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 the end user [3]–[6].”
A Study of the Robustness of KNN Classifiers Trained Using Soft Labels (2006, methodology): “The figures compare the accuracy of the KNN classifier trained with crisp labels and the accuracy of the FKNN classifier trained with the Iris data, K”
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.”
Genetic Fuzzy Systems - Evolutionary Tuning and Learning of Fuzzy Knowledge Bases (2002, methodology): “Pattern recognition constitutes an important application of FRBSs as FRBCSs provide a suitable mean to classify incomplete and imprecise data (Chi, Yan, and Pham, 1996; Kuncheva, 2000).”
Measures of Diversity in Classifier Ensembles and Their Relationship with the Ensemble Accuracy (2003, methodology): “txt (see for more experimental results with the same data (Kuncheva, 2000)).”
Effect of rule weights in fuzzy rule-based classification systems (2000, background): “Kuncheva [21], [22] proved that fuzzy IF–THEN rules with no certainty grades have rectangular or hyperrectangular decision areas when no fuzzy IF–THEN rules are missing in fuzzy rule tables.”
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].”