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.
Product Identifiers
Publisher
Springer International Publishing Ag
ISBN-13
9783319128795
eBay Product ID (ePID)
208901394
Product Key Features
Author
Lech Polkowski, Piotr Artiemjew
Publication Name
Granular Computing in Decision Approximation: an Application of Rough Mereology
Format
Hardcover
Language
English
Subject
Computer Science
Publication Year
2015
Type
Textbook
Number of Pages
452 Pages
Dimensions
Item Height
235mm
Item Width
155mm
Volume
77
Item Weight
8218g
Additional Product Features
Title_Author
Piotr Artiemjew, Lech Polkowski
Series Title
Intelligent Systems Référence Library
Country/Region of Manufacture
Switzerland
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