Granular Computing in Decision Approximation (Record no. 55319)

000 -LEADER
fixed length control field 03587nam a22004815i 4500
001 - CONTROL NUMBER
control field 978-3-319-12880-1
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200421111837.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 150405s2015 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783319128801
-- 978-3-319-12880-1
082 04 - CLASSIFICATION NUMBER
Call Number 006.3
100 1# - AUTHOR NAME
Author Polkowski, Lech.
245 10 - TITLE STATEMENT
Title Granular Computing in Decision Approximation
Sub Title An Application of Rough Mereology /
300 ## - PHYSICAL DESCRIPTION
Number of Pages XV, 452 p. 230 illus.
490 1# - SERIES STATEMENT
Series statement Intelligent Systems Reference Library,
505 0# - FORMATTED CONTENTS NOTE
Remark 2 Similarity and Granulation -- Mereology and Rough Mereology. Rough Mereological Granulation -- Learning data Classification. Classifiers in General and in Decision Systems -- Methodologies for Granular Reflections -- Covering Strategies -- Layered Granulation -- Naive Bayes Classifier on Granular Reflections -- The Case of Concept-Dependent Granulation -- Granular Computing in the Problem of Missing Values -- Granular Classifiers Based on Weak Rough Inclusions -- Effects of Granulation on Entropy and Noise in Data. - Conclusions -- Appendix. Data Characteristics Bearing on Classification.
520 ## - SUMMARY, ETC.
Summary, etc 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.
700 1# - AUTHOR 2
Author 2 Artiemjew, Piotr.
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-3-319-12880-1
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Springer,
-- 2015.
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-- text
-- txt
-- rdacontent
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-- computer
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-- rdamedia
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-- online resource
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-- text file
-- PDF
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650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Engineering.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational intelligence.
650 14 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Engineering.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Computational Intelligence.
650 24 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial Intelligence (incl. Robotics).
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
-- 1868-4394 ;
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-- ZDB-2-ENG

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