E-Book, Englisch, Band 98, 162 Seiten, eBook
Ghosh / Dehuri Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases
2008
ISBN: 978-3-540-77467-9
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, Band 98, 162 Seiten, eBook
Reihe: Studies in Computational Intelligence
ISBN: 978-3-540-77467-9
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark
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.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
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.
