رکورد قبلیرکورد بعدی

" Rough set-based classification systems / "


Document Type : BL
Record Number : 860187
Main Entry : Nowicki, Robert K.
Title & Author : Rough set-based classification systems /\ Robert K. Nowicki.
Publication Statement : Cham, Switzerland :: Springer,, [2019]
Series Statement : Studies in computational intelligence ;; volume 802
Page. NO : 1 online resource
ISBN : 3030038955
: : 3030038963
: : 9783030038953
: : 9783030038960
: 3030038947
: 9783030038946
Bibliographies/Indexes : Includes bibliographical references.
Contents : 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.
Abstract : 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.
Subject : Rough sets.
Subject : Rough sets.
Dewey Classification : ‭511.322‬
LC Classification : ‭QA248‬‭.N69 2019‬
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