Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases

Detalles Bibliográficos
Autor Corporativo:	SpringerLink (Online service)
Otros Autores:	Ghosh, Ashish. (Editor ), Dehuri, Satchidananda. (Editor ), Ghosh, Susmita. (Editor )
Formato:	eBook
Lenguaje:	English
Publicado:	Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2008.
Edición:	1st ed. 2008.
Colección:	Studies in Computational Intelligence, 98
Materias:	Applied mathematics. Engineering mathematics. Artificial intelligence. Mathematical and Computational Engineering. Artificial Intelligence.
Acceso en línea:	https://doi.org/10.1007/978-3-540-77467-9

Tabla de Contenidos:

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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