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Granular Computing in Decision Approximation: An Application of Rough Mereology (Intelligent Systems Reference Library, 77) - Hardcover
This book presents a study in knowledge discovery in data with knowledge understood as a set of relations among objects and their properties. Relations in this case are implicative decision rules and the paradigm in which they are induced is that of computing with granules defined by rough inclusions, the latter introduced and studied within rough mereology, the fuzzified version of mereology. In this book basic classes of rough inclusions are defined and based on them methods for inducing granular structures from data are highlighted. The resulting granular structures are subjected to classifying algorithms, notably k―nearest neighbors and bayesian classifiers.
Experimental results are given in detail both in tabular and visualized form for fourteen data sets from UCI data repository. A striking feature of granular classifiers obtained by this approach is that preserving the accuracy of them on original data, they reduce substantially the size of the granulated data set as well as the set of granular decision rules. This feature makes the presented approach attractive in cases where a small number of rules providing a high classification accuracy is desirable. As basic algorithms used throughout the text are explained and illustrated with hand examples, the book may also serve as a textbook.
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“The book provides an extended presentation of granular computing, focusing on applications in classification/decision theory. ... the book is intended to students and researchers interested in granular computing.” (Florin Gorunescu, zbMATH 1314.68006, 2015)
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- PublisherSpringer
- Publication date2015
- ISBN 10 3319128795
- ISBN 13 9783319128795
- BindingHardcover
- Number of pages467
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Granular Computing in Decision Approximation: An Application of Rough Mereology (Intelligent Systems Reference Library) by Polkowski, Lech, Artiemjew, Piotr [Hardcover ]
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Granular Computing in Decision Approximation: An Application of Rough Mereology (Intelligent Systems Reference Library, 77)
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Book Description Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a study in knowledge discovery in data with knowledge understood as a set of relations among objects and their properties. Relations in this case are implicative decision rules and the paradigm in which they are induced is that of computing with granules defined by rough inclusions, the latter introduced and studied within rough mereology, the fuzzified version of mereology. In this book basic classes of rough inclusions are defined and based on them methods for inducing granular structures from data are highlighted. The resulting granular structures are subjected to classifying algorithms, notably k-nearest neighbors and bayesian classifiers.Experimental results are given in detail both in tabular and visualized form for fourteen data sets from UCI data repository. A striking feature of granular classifiers obtained by this approach is that preserving the accuracy of them on original data, they reduce substantially the size of the granulated data set as well as the set of granular decision rules. This feature makes the presented approach attractive in cases where a small number of rules providing a high classification accuracy is desirable. As basic algorithms used throughout the text are explained and illustrated with hand examples, the book may also serve as a textbook. 468 pp. Englisch. Seller Inventory # 9783319128795
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Book Description Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Recent research on Granular Computing in Decision ApproximationFully develops the topic of granular computing in classifier synthesisPresents applied algorithms which are illustrated with simple hand examplesThis book presents a . Seller Inventory # 24353837
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Book Description Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents a study in knowledge discovery in data with knowledge understood as a set of relations among objects and their properties. Relations in this case are implicative decision rules and the paradigm in which they are induced is that of computing with granules defined by rough inclusions, the latter introduced and studied within rough mereology, the fuzzified version of mereology. In this book basic classes of rough inclusions are defined and based on them methods for inducing granular structures from data are highlighted. The resulting granular structures are subjected to classifying algorithms, notably k-nearest neighbors and bayesian classifiers.Experimental results are given in detail both in tabular and visualized form for fourteen data sets from UCI data repository. A striking feature of granular classifiers obtained by this approach is that preserving the accuracy of them on original data, they reduce substantially the size of the granulated data set as well as the set of granular decision rules. This feature makes the presented approach attractive in cases where a small number of rules providing a high classification accuracy is desirable. As basic algorithms used throughout the text are explained and illustrated with hand examples, the book may also serve as a textbook. Seller Inventory # 9783319128795
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Book Description Hardcover. Condition: Brand New. 2015 edition. 470 pages. 9.50x6.25x1.25 inches. In Stock. Seller Inventory # x-3319128795
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Book Description Condition: New. Granular Computing in Decision Approximation Series: Intelligent Systems Reference Library. Num Pages: 467 pages, 230 black & white illustrations, biography. BIC Classification: UYQ. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 25. Weight in Grams: 859. . 2015. Hardback. . . . . Seller Inventory # V9783319128795
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