國立虎尾科技大學 |

Advances in Feature Selection for Data and Pattern Recognition

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Advances in Feature Selection for Data and Pattern Recognition/ edited by Urszula Stańczyk, Beata Zielosko, Lakhmi C. Jain.
other author:	Stańczyk, Urszula.
Description:	XVIII, 328 p. 37 illus., 20 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Computational intelligence. -
Online resource:	https://doi.org/10.1007/978-3-319-67588-6
ISBN:	9783319675886

Advances in Feature Selection for Data and Pattern Recognition
Advances in Feature Selection for Data and Pattern Recognition[electronic resource] /edited by Urszula Stańczyk, Beata Zielosko, Lakhmi C. Jain. - 1st ed. 2018. - XVIII, 328 p. 37 illus., 20 illus. in color.online resource. - Intelligent Systems Reference Library,1381868-4394 ;. - Intelligent Systems Reference Library,67.

An Introduction -- Attribute Selection Based on Reduction of Numerical Attribute During Discretization -- Improving Bagging Ensembles for Class Imbalanced Data by Active Learning -- Optimization of Decision Rules Relative to Length Based on Modiﬁed Dynamic Programming Approach -- Ranking-Based Rule Classiﬁer Optimisation -- Attribute Selection in a Dispersed Decision-Making System -- Feature Selection Approach for Rule-based Knowledge Bases -- Feature Selection with a Genetic Algorithm for Classiﬁcation of Brain Imaging Data.

This book presents recent developments and research trends in the field of feature selection for data and pattern recognition, highlighting a number of latest advances. The field of feature selection is evolving constantly, providing numerous new algorithms, new solutions, and new applications. Some of the advances presented focus on theoretical approaches, introducing novel propositions highlighting and discussing properties of objects, and analysing the intricacies of processes and bounds on computational complexity, while others are dedicated to the specific requirements of application domains or the particularities of tasks waiting to be solved or improved. Divided into four parts – nature and representation of data; ranking and exploration of features; image, shape, motion, and audio detection and recognition; decision support systems, it is of great interest to a large section of researchers including students, professors and practitioners.

ISBN: 9783319675886

Standard No.: 10.1007/978-3-319-67588-6doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Advances in Feature Selection for Data and Pattern Recognition
LDR:02959nam a22004095i 4500 001 990949
003 DE-He213
005 20200629215301.0
007 cr nn 008mamaa
008 201225s2018 gw | s |||| 0|eng d
020 $a 9783319675886 $9 978-3-319-67588-6
024 7 $a 10.1007/978-3-319-67588-6 $2 doi
035 $a 978-3-319-67588-6
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
245 1 0 $a Advances in Feature Selection for Data and Pattern Recognition $h [electronic resource] / $c edited by Urszula Stańczyk, Beata Zielosko, Lakhmi C. Jain.
250 $a 1st ed. 2018.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a XVIII, 328 p. 37 illus., 20 illus. in color. $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 Intelligent Systems Reference Library, $x 1868-4394 ; $v 138
505 0 $a An Introduction -- Attribute Selection Based on Reduction of Numerical Attribute During Discretization -- Improving Bagging Ensembles for Class Imbalanced Data by Active Learning -- Optimization of Decision Rules Relative to Length Based on Modiﬁed Dynamic Programming Approach -- Ranking-Based Rule Classiﬁer Optimisation -- Attribute Selection in a Dispersed Decision-Making System -- Feature Selection Approach for Rule-based Knowledge Bases -- Feature Selection with a Genetic Algorithm for Classiﬁcation of Brain Imaging Data.
520 $a This book presents recent developments and research trends in the field of feature selection for data and pattern recognition, highlighting a number of latest advances. The field of feature selection is evolving constantly, providing numerous new algorithms, new solutions, and new applications. Some of the advances presented focus on theoretical approaches, introducing novel propositions highlighting and discussing properties of objects, and analysing the intricacies of processes and bounds on computational complexity, while others are dedicated to the specific requirements of application domains or the particularities of tasks waiting to be solved or improved. Divided into four parts – nature and representation of data; ranking and exploration of features; image, shape, motion, and audio detection and recognition; decision support systems, it is of great interest to a large section of researchers including students, professors and practitioners.
650 0 $a Computational intelligence. $3 568984
650 0 $a Artificial intelligence. $3 559380
650 0 $a Pattern recognition. $3 1253525
650 0 $a Data mining. $3 528622
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Pattern Recognition. $3 669796
650 2 4 $a Data Mining and Knowledge Discovery. $3 677765
700 1 $a Stańczyk, Urszula. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1256948
700 1 $a Zielosko, Beata. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 682885
700 1 $a Jain, Lakhmi C. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 671306
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319675879
776 0 8 $i Printed edition: $z 9783319675893
776 0 8 $i Printed edition: $z 9783319884523
830 0 $a Intelligent Systems Reference Library, $x 1868-4394 ; $v 67 $3 1253823
856 4 0 $u https://doi.org/10.1007/978-3-319-67588-6
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)