TY - BOOK AU - ED - SpringerLink (Online service) TI - Multiple Fuzzy Classification Systems T2 - Studies in Fuzziness and Soft Computing, SN - 9783642306044 AV - Q342 U1 - 006.3 23 PY - 2012/// CY - Berlin, Heidelberg PB - Springer Berlin Heidelberg, Imprint: Springer KW - Engineering KW - Computer simulation KW - Optical pattern recognition KW - Computational Intelligence KW - Pattern Recognition KW - Simulation and Modeling N1 - Introduction to fuzzy systems -- Ensemble techniques -- Relational modular fuzzy systems -- Ensembles of the Mamdani fuzzy systems -- Logical type fuzzy systems -- Takagi-Sugeno fuzzy systems -- Roughneurofuzzy Ensembles for Classification with Missing Data -- Concluding remarks and challenges for future research; ZDB-2-ENG N2 - Fuzzy classiers are important tools in exploratory data analysis, which is a vital set of methods used in various engineering, scientic and business applications. Fuzzy classiers use fuzzy rules and do not require assumptions common to statistical classication. Rough set theory is useful when data sets are incomplete. It denes a formal approximation of crisp sets by providing the lower and the upper approximation of the original set. Systems based on rough sets have natural ability to work on such data and incomplete vectors do not have to be preprocessed before classication. To achieve better performance than existing machine learning systems, fuzzy classifiers and rough sets can be combined in ensembles. Such ensembles consist of a nite set of learning models, usually weak learners. The present book discusses the three aforementioned elds fuzzy systems, rough sets and ensemble techniques. As the trained ensemble should represent a single hypothesis, a lot of attention is placed on the possibility to combine fuzzy rules from fuzzy systems being members of classication ensemble. Furthermore, an emphasis is placed on ensembles that can work on incomplete data, thanks to rough set theory UR - http://dx.doi.org/10.1007/978-3-642-30604-4 ER -