國立虎尾科技大學 |

Rough Set–Based Classification Systems

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Rough Set–Based Classification Systems/ by Robert K. Nowicki.
Author:	Nowicki, Robert K.
Description:	XIII, 188 p. 125 illus.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational intelligence. -
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. - 1st ed. 2019. - XIII, 188 p. 125 illus.online resource. - Studies in Computational Intelligence,8021860-949X ;. - Studies in Computational Intelligence,564.

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:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Rough Set–Based Classification Systems
LDR:02723nam a22003975i 4500 001 1010482
003 DE-He213
005 20200630144857.0
007 cr nn 008mamaa
008 210106s2019 gw | s |||| 0|eng d
020 $a 9783030038953 $9 978-3-030-03895-3
024 7 $a 10.1007/978-3-030-03895-3 $2 doi
035 $a 978-3-030-03895-3
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Nowicki, Robert K. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1304565
245 1 0 $a Rough Set–Based Classification Systems $h [electronic resource] / $c by Robert K. Nowicki.
250 $a 1st ed. 2019.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a XIII, 188 p. 125 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Studies in Computational Intelligence, $x 1860-949X ; $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 Computational intelligence. $3 568984
650 0 $a Artificial intelligence. $3 559380
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Artificial Intelligence. $3 646849
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030038946
776 0 8 $i Printed edition: $z 9783030038960
830 0 $a Studies in Computational Intelligence, $x 1860-949X ; $v 564 $3 1253640
856 4 0 $u https://doi.org/10.1007/978-3-030-03895-3
912 $a ZDB-2-INR
912 $a ZDB-2-SXIT
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)
950 $a Intelligent Technologies and Robotics (R0) (SpringerNature-43728)