000 03134nam a22004695i 4500
001 978-3-642-30604-4
003 DE-He213
005 20140220083319.0
007 cr nn 008mamaa
008 120626s2012 gw | s |||| 0|eng d
020 _a9783642306044
_9978-3-642-30604-4
024 7 _a10.1007/978-3-642-30604-4
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aScherer, Rafał.
_eauthor.
245 1 0 _aMultiple Fuzzy Classification Systems
_h[electronic resource] /
_cby Rafał Scherer.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2012.
300 _aXII, 132 p. 24 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Fuzziness and Soft Computing,
_x1434-9922 ;
_v288
505 0 _aIntroduction to fuzzy systems -- Ensemble techniques -- Relational modular fuzzy systems -- Ensembles of the Mamdani fuzzy systems -- Logical type fuzzy systems -- Takagi-Sugeno fuzzy systems -- Rough–neuro–fuzzy Ensembles for Classification with Missing Data -- Concluding remarks and challenges for future research.
520 _aFuzzy classifiers are important tools in exploratory data analysis, which is a vital set of methods used in various engineering, scientific and business applications. Fuzzy classifiers use fuzzy rules and do not require assumptions common to statistical classification. Rough set theory is useful when data sets are incomplete. It defines a formal approximation of crisp sets by providing the lower and the upper approximation of the original set. Systems based on rough sets have natural ability to work on such data and incomplete vectors do not have to be preprocessed before classification. To achieve better performance than existing machine learning systems, fuzzy classifiers and rough sets can be combined in ensembles. Such ensembles consist of a finite set of learning models, usually weak learners. The present book discusses the three aforementioned fields – fuzzy systems, rough sets and ensemble techniques. As the trained ensemble should represent a single hypothesis, a lot of attention is placed on the possibility to combine fuzzy rules from fuzzy systems being members of classification ensemble. Furthermore, an emphasis is placed on ensembles that can work on incomplete data, thanks to rough set theory.
650 0 _aEngineering.
650 0 _aComputer simulation.
650 0 _aOptical pattern recognition.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aPattern Recognition.
650 2 4 _aSimulation and Modeling.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642306037
830 0 _aStudies in Fuzziness and Soft Computing,
_x1434-9922 ;
_v288
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-30604-4
912 _aZDB-2-ENG
999 _c103214
_d103214