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Studies in Computational Intelligence

Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases

Editors: Ghosh, Ashish, Dehuri, Satchidananda, Ghosh, Susmita (Eds.)

  • Assembles high quality original contributions that reflect and advance the state-of-the art in the area of Multi-objective Evolutionary Algorithms for Data Mining and Knowledge Discovery
  • Emphasis on the utility of evolutionary algorithms to various facets of Knowledge Discovery in Databases that involve multiple objectives
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eBook $159.00
price for USA
  • ISBN 978-3-540-77467-9
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $209.00
price for USA
  • ISBN 978-3-540-77466-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $209.00
price for USA
  • ISBN 978-3-642-09615-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

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.

Table of contents (7 chapters)

  • Genetic Algorithm for Optimization of Multiple Objectives in Knowledge Discovery from Large Databases

    Dehuri, Satchidananda (et al.)

    Pages 1-22

  • Knowledge Incorporation in Multi-objective Evolutionary Algorithms

    Landa-Becerra, Ricardo (et al.)

    Pages 23-46

  • Evolutionary Multi-objective Rule Selection for Classification Rule Mining

    Ishibuchi, Hisao (et al.)

    Pages 47-70

  • Rule Extraction from Compact Pareto-optimal Neural Networks

    Jin, Yaochu (et al.)

    Pages 71-90

  • On the Usefulness of MOEAs for Getting Compact FRBSs Under Parameter Tuning and Rule Selection

    Alcalá, R. (et al.)

    Pages 91-107

Buy this book

eBook $159.00
price for USA
  • ISBN 978-3-540-77467-9
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $209.00
price for USA
  • ISBN 978-3-540-77466-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $209.00
price for USA
  • ISBN 978-3-642-09615-0
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases
Editors
  • Ashish Ghosh
  • Satchidananda Dehuri
  • Susmita Ghosh
Series Title
Studies in Computational Intelligence
Series Volume
98
Copyright
2008
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-540-77467-9
DOI
10.1007/978-3-540-77467-9
Hardcover ISBN
978-3-540-77466-2
Softcover ISBN
978-3-642-09615-0
Series ISSN
1860-949X
Edition Number
1
Number of Pages
XIV, 162
Topics