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2020 | OriginalPaper | Chapter

Rough Set Based Supervised Machine Learning Approaches: Survey and Application

Authors : Abdelkhalek Hadrani, Karim Guennoun, Rachid Saadane, Mohammed Wahbi

Published in: Innovations in Smart Cities Applications Edition 3

Publisher: Springer International Publishing

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Abstract

Despite the availability of real, diverse and knowledge-rich data in all domains, they are generally likely to be uncertain, inaccurate, and incomplete. Attracted by the performances and the strong mathematical underpinnings of the Rough Set Theory (RST), many researchers have suggested new efficient methods and algorithms implying RST and able to deal with these aspects characterizing real data. In this paper, we present a survey of rough set based supervised machine learning approaches allowing to induce deterministic or probabilistic decision rules and their different involved methods. The various models of these approaches have been experimentally evaluated and compared in term of prediction accuracy, quality and compactness of the generated decision rules sets when applied to the community-acquired meningitis diagnosis.

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Footnotes
1
These two concepts will be introduced and discussed in Subsect. 3.2.
 
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Metadata
Title
Rough Set Based Supervised Machine Learning Approaches: Survey and Application
Authors
Abdelkhalek Hadrani
Karim Guennoun
Rachid Saadane
Mohammed Wahbi
Copyright Year
2020
Book
Innovations in Smart Cities Applications Edition 3

Print ISBN: 978-3-030-37628-4

Electronic ISBN: 978-3-030-37629-1

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-37629-1_30

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