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Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases

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Paperback, 162 blz. | Engels
Springer Berlin Heidelberg | 0e druk, 2010
ISBN13: 9783642096150
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Springer Berlin Heidelberg 0e druk, 2010 9783642096150
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Samenvatting

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.

Specificaties

ISBN13:9783642096150
Taal:Engels
Bindwijze:paperback
Aantal pagina's:162
Uitgever:Springer Berlin Heidelberg
Druk:0

Inhoudsopgave

Genetic 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.

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        Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases