Accuracy Improvements in Linguistic Fuzzy Modeling

• Corporate Author: SpringerLink (Online service)
• Other Authors: Casillas, Jorge (Editor, http://id.loc.gov/vocabulary/relators/edt), Cordón, O. (Editor, http://id.loc.gov/vocabulary/relators/edt), Herrera Triguero, Francisco (Editor, http://id.loc.gov/vocabulary/relators/edt), Magdalena, Luis (Editor, http://id.loc.gov/vocabulary/relators/edt)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2003.
• Edition: 1st ed. 2003.
• Series: Studies in Fuzziness and Soft Computing, 129
• Subjects: Business mathematics.; Applied mathematics.; Engineering mathematics.; Natural language processing (Computer science).; Artificial intelligence.; Computational complexity.; Economic theory.; Business Mathematics.; Mathematical and Computational Engineering.; Natural Language Processing (NLP).; Artificial Intelligence.; Complexity.; Economic Theory/Quantitative Economics/Mathematical Methods.

Fuzzy modeling usually comes with two contradictory requirements: interpretability, which is the capability to express the real system behavior in a comprehensible way, and accuracy, which is the capability to faithfully represent the real system. In this framework, one of the most important areas i...

Table of Contents:

• Overview
• Accuracy Improvements to Find the Balance Interpretability-Accuracy in Linguistic Fuzzy Modeling: An Overview
• Accuracy Improvements Constrained by Interpretability Criteria
• COR Methodology: A Simple Way to Obtain Linguistic Fuzzy Models with Good Interpretability and Accuracy
• Constrained optimization of genetic fuzzy systems
• Trade-off between the Number of Fuzzy Rules and Their Classification Performance
• Generating distinguishable, complete, consistent and compact fuzzy systems using evolutionary algorithms
• Fuzzy CoCo: Balancing Accuracy and Interpretability of Fuzzy Models by Means of Coevolution
• On the Achievement of Both Accurate and Interpretable Fuzzy Systems Using Data-Driven Design Processes
• Extending the Modeling Process to Improve the Accuracy
• Linguistic Hedges and Fuzzy Rule Based Systems
• Automatic Construction of Fuzzy Rule-Based Systems: A trade-off between complexity and accuracy maintaining interpretability
• Using Individually Tested Rules for the Data-based Generation of Interpretable Rule Bases with High Accuracy
• Extending the Model Structure to Improve the Accuracy
• A description of several characteristics for improving the accuracy and interpretability of inductive linguistic rule learning algorithms
• An Iterative Learning Methodology to Design Hierarchical Systems of Linguistic Rules for Linguistic Modeling
• Learning Default Fuzzy Rules with General and Punctual Exceptions
• Integration of Fuzzy Knowledge
• Tuning fuzzy partitions or assigning weights to fuzzy rules: which is better?.