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020 _a9789813291669
_9978-981-32-9166-9
024 7 _a10.1007/978-981-32-9166-9
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
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082 0 4 _a006.3
_223
100 1 _aRaza, Muhammad Summair.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aUnderstanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications
_h[electronic resource] /
_cby Muhammad Summair Raza, Usman Qamar.
250 _a2nd ed. 2019.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2019.
300 _aXVI, 236 p. 147 illus., 27 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction to Feature Selection -- Background -- Rough Set Theory -- Advance Concepts in Rough Set Theory -- Rough Set Theory Based Feature Selection Techniques -- Chapter 6: Unsupervised Feature Selection using RST -- Critical Analysis of Feature Selection Algorithms -- Dominance based Rough Set Approach -- Fuzzy Rough Sets -- Introduction to classicial Rough Set Based APIs Library -- Dominance Based Rough Set APIs library.
520 _aThis book provides a comprehensive introduction to rough set-based feature selection. Rough set theory, first proposed by Zdzislaw Pawlak in 1982, continues to evolve. Concerned with the classification and analysis of imprecise or uncertain information and knowledge, it has become a prominent tool for data analysis, and enables the reader to systematically study all topics in rough set theory (RST) including preliminaries, advanced concepts, and feature selection using RST. The book is supplemented with an RST-based API library that can be used to implement several RST concepts and RST-based feature selection algorithms. The book provides an essential reference guide for students, researchers, and developers working in the areas of feature selection, knowledge discovery, and reasoning with uncertainty, especially those who are working in RST and granular computing. The primary audience of this book is the research community using rough set theory (RST) to perform feature selection (FS) on large-scale datasets in various domains. However, any community interested in feature selection such as medical, banking, and finance can also benefit from the book. This second edition also covers the dominance-based rough set approach and fuzzy rough sets. The dominance-based rough set approach (DRSA) is an extension of the conventional rough set approach and supports the preference order using the dominance principle. In turn, fuzzy rough sets are fuzzy generalizations of rough sets. An API library for the DRSA is also provided with the second edition of the book.
650 0 _aArtificial intelligence.
650 0 _aApplication software.
650 0 _aDatabase management.
650 0 _aData mining.
650 0 _aNumerical analysis.
650 1 4 _aArtificial Intelligence.
650 2 4 _aComputer and Information Systems Applications.
650 2 4 _aDatabase Management.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aNumerical Analysis.
700 1 _aQamar, Usman.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789813291652
776 0 8 _iPrinted edition:
_z9789813291676
776 0 8 _iPrinted edition:
_z9789813291683
856 4 0 _uhttps://doi.org/10.1007/978-981-32-9166-9
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c172704
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