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Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases [electronic resource] /edited by Ashish Ghosh, Satchidananda Dehuri, Susmita Ghosh.

by Ghosh, Ashish [editor.]; Dehuri, Satchidananda [editor.]; Ghosh, Susmita [editor.]; SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence: 98Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.Description: online resource.ISBN: 9783540774679.Subject(s): Engineering | Artificial intelligence | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics)DDC classification: 519 Online resources: Click here to access online
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.
In: Springer eBooksSummary: 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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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.

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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