Interpretability Issues in Fuzzy Modeling

✍ Scribed by Jorge Casillas, Oscar Cordón, Francisco Herrera, Luis Magdalena (auth.), Dr. Jorge Casillas, Dr. Oscar Cordón, Dr. Francisco Herrera, Dr. Luis Magdalena (eds.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2003
• Tongue: English
• Leaves: 646
• Series: Studies in Fuzziness and Soft Computing 128
• Edition: 1
• Category: Library

✦ Synopsis

Fuzzy modeling has become one of the most productive and successful results of fuzzy logic. Among others, it has been applied to knowledge discovery, automatic classification, long-term prediction, or medical and engineering analysis. The research developed in the topic during the last two decades has been mainly focused on exploiting the fuzzy model flexibility to obtain the highest accuracy. This approach usually sets aside the interpretability of the obtained models. However, we should remember the initial philosophy of fuzzy sets theory directed to serve the bridge between the human understanding and the machine processing. In this challenge, the ability of fuzzy models to express the behavior of the real system in a comprehensible manner acquires a great importance. This book collects the works of a group of experts in the field that advocate the interpretability improvements as a mechanism to obtain well balanced fuzzy models.

✦ Table of Contents

Front Matter....Pages I-XIV
Front Matter....Pages 1-1
Interpretability Improvements to Find the Balance Interpretability-Accuracy in Fuzzy Modeling: An Overview....Pages 3-22
Front Matter....Pages 23-23
Regaining Comprehensibility of Approximative Fuzzy Models via the Use of Linguistic Hedges....Pages 25-53
Identifying Flexible Structured Premises for Mining Concise Fuzzy Knowledge....Pages 54-76
Front Matter....Pages 77-77
A Multiobjective Genetic Learning Process for joint Feature Selection and Granularity and Contexts Learning in Fuzzy Rule-Based Classification Systems....Pages 79-99
Extracting Linguistic Fuzzy Models from Numerical Data-AFRELI Algorithm....Pages 100-124
Constrained Optimization of Fuzzy Decision Trees....Pages 125-147
A new method for inducing a set of interpretable fuzzy partitions and fuzzy inference systems from data....Pages 148-175
A Feature Ranking Algorithm for Fuzzy Modelling Problems....Pages 176-192
Interpretability in Multidimensional Classification....Pages 193-217
Front Matter....Pages 219-219
Interpretable Semi-Mechanistic Fuzzy Models by Clustering, OLS and FIS Model Reduction....Pages 221-248
Trade-off between approximation accuracy and complexity: TS controller design via HOSVD based complexity minimization....Pages 249-277
Simplification and reduction of fuzzy rules....Pages 278-302
Effect of Rule Representation in Rule Base Reduction....Pages 303-324
Singular Value-Based Fuzzy Reduction With Relaxed Normality Condition....Pages 325-352
Front Matter....Pages 353-353
Interpretability, Complexity, and Modular Structure of Fuzzy Systems....Pages 355-378
Hierarchical Genetic Fuzzy Systems: Accuracy, Interpretability and Design Autonomy....Pages 379-405
About the trade-off between accuracy and interpretability of Takagi-Sugeno models in the context of nonlinear time series forecasting....Pages 406-430
Accurate, transparent and compact fuzzy models by multi-objective evolutionary algorithms....Pages 431-451
Transparent Fuzzy Systems in Modelling and Control....Pages 452-476
Uniform Fuzzy Partitions with Cardinal Splines and Wavelets: Getting Interpretable Linguistic Fuzzy Models....Pages 477-495
Front Matter....Pages 497-497
Relating the theory of partitions in MV-logic to the design of interpretable fuzzy systems....Pages 499-523
A Formal Model of Interpretability of Linguistic Variables....Pages 524-545
Expressing Relevance Interpretability and Accuracy of Rule-Based Systems....Pages 546-567
Conciseness of Fuzzy Models....Pages 568-586
Exact trade-off between approximation accuracy and interpretability: solving the saturation problem for certain FRBSs....Pages 587-601
Front Matter....Pages 603-603
Interpretability improvement of RBF-based neurofuzzy systems using regularized learning....Pages 605-620
Extracting Fuzzy Classification Rules from Fuzzy Clusters on the Basis of Separating Hyperplanes....Pages 621-643

✦ Subjects

Complexity;Artificial Intelligence (incl. Robotics);Economic Theory;Operation Research/Decision Theory;Business Information Systems