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020 _a9783319128801
_9978-3-319-12880-1
024 7 _a10.1007/978-3-319-12880-1
_2doi
035 _ato000558357
040 _aSpringer
_cSpringer
_dRU-ToGU
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aPolkowski, Lech.
_eauthor.
_9328333
245 1 0 _aGranular Computing in Decision Approximation
_helectronic resource
_bAn Application of Rough Mereology /
_cby Lech Polkowski, Piotr Artiemjew.
260 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aXV, 452 p. 230 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aIntelligent Systems Reference Library,
_x1868-4394 ;
_v77
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 _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.
650 0 _aengineering.
_9224332
650 0 _aArtificial intelligence.
_9274099
650 0 _aComputational Intelligence.
_9307538
650 1 4 _aEngineering.
_9224332
650 2 4 _aComputational Intelligence.
_9307538
650 2 4 _aArtificial Intelligence (incl. Robotics).
_9274102
700 1 _aArtiemjew, Piotr.
_eauthor.
_9464541
710 2 _aSpringerLink (Online service)
_9143950
773 0 _tSpringer eBooks
830 0 _aIntelligent Systems Reference Library,
_9447053
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-12880-1
912 _aZDB-2-ENG
999 _c413337