Overview
- Allows the reader to successfully work with sets of indistinguishable values and missing values
- Develops decision-making systems in two configurations: iterative and collective
- Written by respected experts in the field
Part of the book series: Studies in Computational Intelligence (SCI, volume 802)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (8 chapters)
Keywords
About this book
The proposed classifiers are applied and tested in two configurations: The first is an iterative mode in which a single classification system requests completion of the input data until an unequivocal decision (classification) is obtained. It allows us to start classification processes using very limited input data and supplementing it only as needed, which limits the cost of obtaining data. The second configuration is an ensemble mode in which several rough set-based classification systems achieve the unequivocal decision collectively, even though the systems cannot separately deliver such results.
Authors and Affiliations
Bibliographic Information
Book Title: Rough Set–Based Classification Systems
Authors: Robert K. Nowicki
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-030-03895-3
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-03894-6Published: 05 February 2019
eBook ISBN: 978-3-030-03895-3Published: 17 December 2018
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XIII, 188
Number of Illustrations: 125 b/w illustrations