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Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications (Hardcover, 2nd ed. 2019)
Loot Price: R3,816
Discovery Miles 38 160
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Understanding and Using Rough Set Based Feature Selection: Concepts, Techniques and Applications (Hardcover, 2nd ed. 2019)
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This 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.
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