Hedge algebras: an algebraic approach to structure of sets of linguistic truth values
Fuzzy Sets and Systems
Fuzzy equalization in the construction of fuzzy sets
Fuzzy Sets and Systems
Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on soft computing for information mining
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
A weighting function for improving fuzzy classification systems performance
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Parallel distributed genetic fuzzy rule selection
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Genetic Fuzzy Systems: Recent Developments and Future Directions; Guest editors: Jorge Casillas, Brian Carse
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Soft Computing - A Fusion of Foundations, Methodologies and Applications
International Journal of Approximate Reasoning
Engineering Evolutionary Intelligent Systems
Engineering Evolutionary Intelligent Systems
Information Sciences: an International Journal
Interpretability assessment of fuzzy knowledge bases: A cointension based approach
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures
Information Sciences: an International Journal
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Intelligent Systems, Design and Applications (ISDA 2009)
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary Fuzzy Systems
Design methodologies of fuzzy set-based fuzzy model based on GAs and information granulation
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Linguistic models and linguistic modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Effect of rule weights in fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Generating the knowledge base of a fuzzy rule-based system by the genetic learning of the data base
IEEE Transactions on Fuzzy Systems
Rule Weight Specification in Fuzzy Rule-Based Classification Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
SGERD: A Steady-State Genetic Algorithm for Extracting Fuzzy Classification Rules From Data
IEEE Transactions on Fuzzy Systems
An interpretable classification rule mining algorithm
Information Sciences: an International Journal
An efficient multi-objective evolutionary fuzzy system for regression problems
International Journal of Approximate Reasoning
A construction of sound semantic linguistic scales using 4-tuple representation of term semantics
International Journal of Approximate Reasoning
Information Sciences: an International Journal
Hi-index | 0.00 |
The determination of fuzzy information granules including the estimation of their membership functions play a significant role in fuzzy system design as well as in the design of fuzzy rule based classifiers (FRBCSs). However, although linguistic terms are fundamental elements in the process of elucidating expert's knowledge, the problem of linguistic term design along with their fuzzy-set-based semantics has not been fully addressed, since term-sets of attributes have not been interpreted as a formalized structure. Thus, the essential relationship between linguistic terms, as syntax, and the constructed fuzzy sets, as their quantitative semantics, or in other words, the problem of the natural semantics of terms behind the linguistic literal has not been addressed. In this paper, we introduce the problem of the design of optimal linguistic terms and propose a method of the design of FRBCSs which may incorporate with the design of linguistic terms to ensure that the presence of linguistic literals are supported not only by data but also by their natural semantics. It is shown that this problem plays a primordial role in enhancing the performance and the interpretability of the designed FRBCSs and helps striking a better balance between the generality and the specificity of the desired fuzzy rule bases for fuzzy classification problems. A series of experiments concerning 17 Machine Learning datasets is reported.