Buch, Englisch, Band 98, 162 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 277 g
Buch, Englisch, Band 98, 162 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 277 g
Reihe: Studies in Computational Intelligence
ISBN: 978-3-642-09615-0
Verlag: Springer
Data 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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
- Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
- Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computer-Aided Design (CAD)
- Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken
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.
