000 03278nam a22004815i 4500
001 978-3-642-02541-9
003 DE-He213
005 20140220084523.0
007 cr nn 008mamaa
008 100301s2010 gw | s |||| 0|eng d
020 _a9783642025419
_9978-3-642-02541-9
024 7 _a10.1007/978-3-642-02541-9
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
082 0 4 _a006.312
_223
100 1 _aPappa, Gisele L.
_eauthor.
245 1 0 _aAutomating the Design of Data Mining Algorithms
_h[electronic resource] :
_bAn Evolutionary Computation Approach /
_cby Gisele L. Pappa, Alex Freitas.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2010.
300 _aXIII, 187p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aNatural Computing Series,
_x1619-7127
505 0 _aData Mining -- Evolutionary Algorithms -- Genetic Programming for Classification and Algorithm Design -- Automating the Design of Rule Induction Algorithms -- Computational Results on the Automatic Design of Full Rule Induction Algorithms -- Directions for Future Research on the Automatic Design of Data Mining Algorithms.
520 _aTraditionally, evolutionary computing techniques have been applied in the area of data mining either to optimize the parameters of data mining algorithms or to discover knowledge or patterns in the data, i.e., to directly solve the target data mining problem. This book proposes a different goal for evolutionary algorithms in data mining: to automate the design of a data mining algorithm, rather than just optimize its parameters. The authors first offer introductory overviews on data mining, focusing on rule induction methods, and on evolutionary algorithms, focusing on genetic programming. They then examine the conventional use of evolutionary algorithms to discover classification rules or related types of knowledge structures in the data, before moving to the more ambitious objective of their research, the design of a new genetic programming system for automating the design of full rule induction algorithms. They analyze computational results from their automatically designed algorithms, which show that the machine-designed rule induction algorithms are competitive when compared with state-of-the-art human-designed algorithms. Finally the authors examine future research directions. This book will be useful for researchers and practitioners in the areas of data mining and evolutionary computation.
650 0 _aComputer science.
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 1 4 _aComputer Science.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aFreitas, Alex.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642025402
830 0 _aNatural Computing Series,
_x1619-7127
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-02541-9
912 _aZDB-2-SCS
999 _c111420
_d111420