Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases

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Ashish Ghosh, Satchidananda Dehuri, Susmita Ghosh
Springer Science & Business Media, Mar 19, 2008 - Mathematics - 162 pages

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.

 

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Contents

Genetic Algorithm for Optimization of Multiple Objectives in Knowledge Discovery from Large Databases
1
Knowledge Incorporation in Multiobjective Evolutionary Algorithms
23
Evolutionary Multiobjective Rule Selection for Classification Rule Mining
47
Rule Extraction from Compact Paretooptimal Neural Networks
71
On the Usefulness of MOEAs for Getting Compact FRBSs Under Parameter Tuning and Rule Selection
91
Classification and Survival Analysis Using Multiobjective Evolutionary Algorithms
108
Clustering Based on Genetic Algorithms
137
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