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000726421 020__ $$a9783319128801$$qelectronic book
000726421 020__ $$a3319128809$$qelectronic book
000726421 020__ $$z9783319128795
000726421 0247_ $$a10.1007/978-3-319-12880-1$$2doi
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000726421 049__ $$aISEA
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000726421 08204 $$a511.3/22$$223
000726421 1001_ $$aPolkowski, Lech,$$eauthor.
000726421 24500 $$aGranular computing in decision approximation$$h[electronic resource] :$$ban application of rough mereology /$$cLech Polkowski, Piotr Artiemjew.
000726421 264_1 $$aCham :$$bSpringer,$$c[2015]
000726421 300__ $$a1 online resource :$$billustrations.
000726421 336__ $$atext$$btxt$$2rdacontent
000726421 337__ $$acomputer$$bc$$2rdamedia
000726421 338__ $$aonline resource$$bcr$$2rdacarrier
000726421 4901_ $$aIntelligent systems reference library,$$x1868-4408 ;$$vvolume 77
000726421 504__ $$aIncludes bibliographical references and indexes.
000726421 5050_ $$aSimilarity and Granulation -- Mereology and Rough Mereology. Rough Mereological Granulation -- Learning data Classification. Classifiers in General and in Decision Systems -- Methodologies for Granular Reflections -- Covering Strategies -- Layered Granulation -- Naive Bayes Classifier on Granular Reflections -- The Case of Concept-Dependent Granulation -- Granular Computing in the Problem of Missing Values -- Granular Classifiers Based on Weak Rough Inclusions -- Effects of Granulation on Entropy and Noise in Data. -- Conclusions -- Appendix. Data Characteristics Bearing on Classification.
000726421 506__ $$aAccess limited to authorized users.
000726421 520__ $$aThis book presents a study in knowledge discovery in data with knowledge understood as a set of relations among objects and their properties. Relations in this case are implicative decision rules and the paradigm in which they are induced is that of computing with granules defined by rough inclusions, the latter introduced and studied within rough mereology, the fuzzified version of mereology. In this book basic classes of rough inclusions are defined and based on them methods for inducing granular structures from data are highlighted. The resulting granular structures are subjected to classifying algorithms, notably k?nearest neighbors and bayesian classifiers. Experimental results are given in detail both in tabular and visualized form for fourteen data sets from UCI data repository. A striking feature of granular classifiers obtained by this approach is that preserving the accuracy of them on original data, they reduce substantially the size of the granulated data set as well as the set of granular decision rules. This feature makes the presented approach attractive in cases where a small number of rules providing a high classification accuracy is desirable. As basic algorithms used throughout the text are explained and illustrated with hand examples, the book may also serve as a textbook.
000726421 588__ $$aOnline resource; title from PDF title page (viewed April 9, 2015).
000726421 650_0 $$aRough sets.
000726421 650_0 $$aWhole and parts (Philosophy)
000726421 650_0 $$aApproximation theory.
000726421 7001_ $$aArtiemjew, Piotr,$$eauthor.
000726421 77608 $$iPrint version:$$aPolkowski, Lech.$$tGranular computing in decision approximation.$$dCham, [Germany] ; Heidelberg, [Germany] : Springer International Publishing, c2015$$z9783319128795$$w2014953920
000726421 830_0 $$aIntelligent systems reference library ;$$vv. 77.
000726421 852__ $$bebk
000726421 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-12880-1$$zOnline Access$$91397441.1
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