Multi-objective evolutionary algorithms for knowledge discovery from 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 objectivessuch 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). 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 Emphasizes on the utility of evolutionary algorithms to various facets of Knowledge Discovery in Databases that involve multiple objectives INDICE: 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 NeuralNetworks.- On the Usefulness of MOEAs for Getting Compact FRBSs Under Parameter Tuning and Rule Selection.- Classification and Survival Analysis Using Multi-objective.- Clustering Based on Genetic Algorithms.

• ISBN: 978-3-540-77466-2
• Editorial: Springer
• Encuadernacion: Cartoné
• Páginas: 160
• Fecha Publicación: 01/02/2008
• Nº Volúmenes: 1
• Idioma: Inglés