Multiple Fuzzy Classification Systems

• Main Author: Scherer, Rafał (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012.
• Series: Studies in Fuzziness and Soft Computing, 288
• Subjects: Engineering.; Computer simulation.; Pattern recognition.; Computational intelligence.; Computational Intelligence.; Pattern Recognition.; Simulation and Modeling.

Fuzzy classifiers are important tools in exploratory data analysis, which is a vital set of methods used in various engineering, scientific and business applications. Fuzzy classifiers use fuzzy rules and do not require assumptions common to statistical classification. Rough set theory is useful when da...

Table of Contents:

• Introduction to fuzzy systems
• Ensemble techniques
• Relational modular fuzzy systems
• Ensembles of the Mamdani fuzzy systems
• Logical type fuzzy systems
• Takagi-Sugeno fuzzy systems
• Rough–neuro–fuzzy Ensembles for Classification with Missing Data
• Concluding remarks and challenges for future research.