Ebook: Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases
Author: Satchidananda Dehuri Susmita Ghosh (auth.) Ashish Ghosh Satchidananda Dehuri Susmita Ghosh (eds.)
- Tags: Appl.Mathematics/Computational Methods of Engineering, Artificial Intelligence (incl. Robotics)
- Series: Studies in Computational Intelligence 98
- Year: 2008
- Publisher: Springer-Verlag Berlin Heidelberg
- Edition: 1
- Language: English
- pdf
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.
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.
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.
Content:
Front Matter....Pages i-xiv
Genetic Algorithm for Optimization of Multiple Objectives in Knowledge Discovery from Large Databases....Pages 1-22
Knowledge Incorporation in Multi-objective Evolutionary Algorithms....Pages 23-46
Evolutionary Multi-objective Rule Selection for Classification Rule Mining....Pages 47-70
Rule Extraction from Compact Pareto-optimal Neural Networks....Pages 71-90
On the Usefulness of MOEAs for Getting Compact FRBSs Under Parameter Tuning and Rule Selection....Pages 91-107
Classification and Survival Analysis Using Multi-objective Evolutionary Algorithms....Pages 109-135
Clustering Based on Genetic Algorithms....Pages 137-159
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.
Content:
Front Matter....Pages i-xiv
Genetic Algorithm for Optimization of Multiple Objectives in Knowledge Discovery from Large Databases....Pages 1-22
Knowledge Incorporation in Multi-objective Evolutionary Algorithms....Pages 23-46
Evolutionary Multi-objective Rule Selection for Classification Rule Mining....Pages 47-70
Rule Extraction from Compact Pareto-optimal Neural Networks....Pages 71-90
On the Usefulness of MOEAs for Getting Compact FRBSs Under Parameter Tuning and Rule Selection....Pages 91-107
Classification and Survival Analysis Using Multi-objective Evolutionary Algorithms....Pages 109-135
Clustering Based on Genetic Algorithms....Pages 137-159
....