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Rough set-based classification systems

Nowicki, Robert K.

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Rough set-based classification systems

Record Type:	Electronic resources : Monograph/item
Title/Author:	Rough set-based classification systems/ by Robert K. Nowicki.
Author:	Nowicki, Robert K.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	xiii, 188 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Rough Set Theory Fundamentals -- Rough Fuzzy Classiﬁcation Systems -- Fuzzy Rough Classiﬁcation Systems -- Rough Neural Network Classiﬁer -- Rough Nearest Neighbour Classiﬁer -- Ensembles of Rough Set-Based Classiﬁers -- Final Remarks.
Contained By:	Springer eBooks
Subject:	Rough sets. -
Online resource:	https://doi.org/10.1007/978-3-030-03895-3
ISBN:	9783030038953

Rough set-based classification systems
Nowicki, Robert K.

Rough set-based classification systems[electronic resource] /by Robert K. Nowicki. - Cham :Springer International Publishing :2019. - xiii, 188 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.8021860-949X ;. - Studies in computational intelligence ;v.802..

Introduction -- Rough Set Theory Fundamentals -- Rough Fuzzy Classiﬁcation Systems -- Fuzzy Rough Classiﬁcation Systems -- Rough Neural Network Classiﬁer -- Rough Nearest Neighbour Classiﬁer -- Ensembles of Rough Set-Based Classiﬁers -- Final Remarks.

This book demonstrates an original concept for implementing the rough set theory in the construction of decision-making systems. It addresses three types of decisions, including those in which the information or input data is insufficient. Though decision-making and classification in cases with missing or inaccurate data is a common task, classical decision-making systems are not naturally adapted to it. One solution is to apply the rough set theory proposed by Prof. Pawlak. 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.

ISBN: 9783030038953

Standard No.: 10.1007/978-3-030-03895-3doiSubjects--Topical Terms:

577805
Rough sets.

LC Class. No.: QA248 / .N69 2019

Dewey Class. No.: 511.322

Rough set-based classification systems
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100 1 $a Nowicki, Robert K. $3 3383363
245 1 0 $a Rough set-based classification systems $h [electronic resource] / $c by Robert K. Nowicki.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xiii, 188 p. : $b ill., digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-949X ; $v v.802
505 0 $a Introduction -- Rough Set Theory Fundamentals -- Rough Fuzzy Classiﬁcation Systems -- Fuzzy Rough Classiﬁcation Systems -- Rough Neural Network Classiﬁer -- Rough Nearest Neighbour Classiﬁer -- Ensembles of Rough Set-Based Classiﬁers -- Final Remarks.
520 $a This book demonstrates an original concept for implementing the rough set theory in the construction of decision-making systems. It addresses three types of decisions, including those in which the information or input data is insufficient. Though decision-making and classification in cases with missing or inaccurate data is a common task, classical decision-making systems are not naturally adapted to it. One solution is to apply the rough set theory proposed by Prof. Pawlak. 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.
650 0 $a Rough sets. $3 577805
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830 0 $a Studies in computational intelligence ; $v v.802. $3 3383364
856 4 0 $u https://doi.org/10.1007/978-3-030-03895-3
950 $a Intelligent Technologies and Robotics (Springer-42732)