000			03680nam a22004095i 4500
999			_c103846 _d103846 _x1
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003			DE-He213
005			20230102113148.0
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007			cr nn 008mamaa
008			150405s2015 gw | s |||| 0|eng d
020			_a9783319128801
024	7		_a10.1007/978-3-319-12880-1 _2doi
040			_bspa _dES-MaUEC
050		4	_aQA248 _b2015 EB
100	1		_aPolkowski, Lech. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/n98051766 _1http://viaf.org/viaf/191415471/
245	1	0	_aGranular Computing in Decision Approximation _bAn Application of Rough Mereology _cby Lech Polkowski, Piotr Artiemjew.
264		1	_aCham _bSpringer International Publishing _c2015
300			_a1 recurso en línea (XV, 452 páginas 230 ilustraciones)
336			_2rdacontent _aTexto (visual) _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
490	0		_aIntelligent Systems Reference Library, _x1868-4394 ; _v77
490	0		_aEngineering (Springer-11647)
505	0		_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.
520	3		_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.
988			_aEBSPRINGER_2018
650		7	_aConjuntos, Teoría de _2embne _9405124
650		7	_9668191 _aNúmeros transfinitos
700	1		_aArtiemjew, Piotr. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/305293408/
776	0	8	_iEdición impresa: _z9783319128818
776	0	8	_iEdición impresa: _z9783319128795
776	0	8	_iEdición impresa: _z9783319366210
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-12880-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b04/2019 _dz _eIG _zSI