Overview
- Novel approach for exploratory data analysis with ensembles of various neuro-fuzzy systems
- Derivation of various ensemble architectures that are able to
- work with missing data
- Written by an expert in this field
Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 288)
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Table of contents (9 chapters)
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Front Matter
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Back Matter
Keywords
About this book
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 data sets are incomplete. It defines 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 classification. To achieve better performance than existing machine learning systems, fuzzy classifiers and rough sets can be combined in ensembles. Such ensembles consist of a finite set of learning models, usually weak learners.
The present book discusses the three aforementioned fields – 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 classification ensemble. Furthermore, an emphasis is placed on ensembles that can work on incomplete data, thanks to rough set theory.
.Authors and Affiliations
Bibliographic Information
Book Title: Multiple Fuzzy Classification Systems
Authors: Rafał Scherer
Series Title: Studies in Fuzziness and Soft Computing
DOI: https://doi.org/10.1007/978-3-642-30604-4
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2012
Hardcover ISBN: 978-3-642-30603-7Published: 28 June 2012
Softcover ISBN: 978-3-642-43657-4Published: 18 July 2014
eBook ISBN: 978-3-642-30604-4Published: 26 June 2012
Series ISSN: 1434-9922
Series E-ISSN: 1860-0808
Edition Number: 1
Number of Pages: XII, 132
Topics: Computational Intelligence, Pattern Recognition, Simulation and Modeling