000 03371nam a22005055i 4500
001 978-3-540-77467-9
003 DE-He213
005 20161121230546.0
007 cr nn 008mamaa
008 100301s2008 gw | s |||| 0|eng d
020 _a9783540774679
_9978-3-540-77467-9
024 7 _a10.1007/978-3-540-77467-9
_2doi
050 4 _aTA329-348
050 4 _aTA640-643
072 7 _aTBJ
_2bicssc
072 7 _aMAT003000
_2bisacsh
082 0 4 _a519
_223
245 1 0 _aMulti-Objective Evolutionary Algorithms for Knowledge Discovery from Databases
_h[electronic resource] /
_cedited by Ashish Ghosh, Satchidananda Dehuri, Susmita Ghosh.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2008.
300 _aXIV, 162 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v98
505 0 _aGenetic Algorithm for Optimization of Multiple Objectives in Knowledge Discovery from Large Databases -- Knowledge Incorporation in Multi-objective Evolutionary Algorithms -- Evolutionary Multi-objective Rule Selection for Classification Rule Mining -- Rule Extraction from Compact Pareto-optimal Neural Networks -- On the Usefulness of MOEAs for Getting Compact FRBSs Under Parameter Tuning and Rule Selection -- Classification and Survival Analysis Using Multi-objective Evolutionary Algorithms -- Clustering Based on Genetic Algorithms.
520 _aData Mining (DM) is the most commonly used name to describe such computational analysis of data and the results obtained must conform to several objectives such as accuracy, comprehensibility, interest for the user etc. Though there are many sophisticated techniques developed by various interdisciplinary fields only a few of them are well equipped to handle these multi-criteria issues of DM. Therefore, the DM issues have attracted considerable attention of the well established multiobjective genetic algorithm community to optimize the objectives in the tasks of DM. The present volume provides a collection of seven articles containing new and high quality research results demonstrating the significance of Multi-objective Evolutionary Algorithms (MOEA) for data mining tasks in Knowledge Discovery from Databases (KDD). These articles are written by leading experts around the world. It is shown how the different MOEAs can be utilized, both in individual and integrated manner, in various ways to efficiently mine data from large databases.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aApplied mathematics.
650 0 _aEngineering mathematics.
650 1 4 _aEngineering.
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aGhosh, Ashish.
_eeditor.
700 1 _aDehuri, Satchidananda.
_eeditor.
700 1 _aGhosh, Susmita.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540774662
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v98
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-540-77467-9
912 _aZDB-2-ENG
950 _aEngineering (Springer-11647)
999 _c500556
_d500556