Effect of rule weights in fuzzy rule-based classification systems

By Hisao Ishibuchi, Tomoharu Nakashima. Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063), 2000

501 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/699da69bee36f52d7bda11d83e98d0175e9793ad

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computational Theory and Mathematics

Citations in the corpus

Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics (2012, background): “…for our FRBCSs: Rule Rj : If x1 is Aj1 and . . . and xn is Ajn then Class ¼ Cj with RWj ð9Þ where Rj is the label of the jth rule, x = (x1, . . . ,xn) is an n-dimensional pattern vector, Aji is an antecedent fuzzy set, Cj is a class label, and RWj is the rule weight (Ishibuchi & Nakashima, 2001).”
FURIA: an algorithm for unordered fuzzy rule induction (2009, background): “Ishibuchi and Nakashima (2001); Ishibuchi and Yamamoto (2005) argued that weighing rules according to (4) allows for modeling more flexible decision boundaries and thereby improves classification accuracy.”
A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets (2008, methodology): “In the specialized literature rule weights have been used in order to improve the performance of FRBCSs [22], where the most common definition is the CF [7], named in some papers as “confidence” [26,47]:”
Fuzzy rule selection by multi-objective genetic local search algorithms and rule evaluation measures in data mining (2004, methodology): “systems [ 10-16 ]. Let S be the set of the selected N fuzzy if-then rules.”
A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems With Genetic Rule Selection and Lateral Tuning (2011, background): “The rule weight of each fuzzy rule Rj has a great effect on the performance of fuzzy rule-based classifiers [ 44 ].”
Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning (2007, background): “Rule weights have a significant effect on the classification accuracy of a fuzzy rule-based classifier [26].”
Rule weight specification in fuzzy rule-based classification systems (2005, methodology): “equivalent to the modification of the heights of antecedent fuzzy sets [ 1 ], [8].”