Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases / Edition 1
by Ashish GhoshView All Available Formats & Editions
ISBN-10: 3642096158
ISBN-13: 9783642096150
Pub. Date: 11/19/2010
Publisher: Springer Berlin Heidelberg
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
Overview
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.
Product Details
- ISBN-13:
- 9783642096150
- Publisher:
- Springer Berlin Heidelberg
- Publication date:
- 11/19/2010
- Series:
- Studies in Computational Intelligence Series , #98
- Edition description:
- Softcover reprint of hardcover 1st ed. 2008
- Pages:
- 162
- Product dimensions:
- 6.14(w) x 9.21(h) x 0.38(d)
Table of Contents
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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